prompt stringlengths 3.22k 3.46k | chosen stringlengths 2.6k 2.88k | response stringlengths 2.62k 2.71k | gpt_score float64 -4 3 ⌀ |
|---|---|---|---|
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prioritize content suitable for both young and old audiences.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning agent.... | state * (agent_feats[7] + agent_feats[11] + 2 * (agent_feats[36] or agent_feats[42]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.34%
Enrollment gestational age (11-20): 17.22%
Enrollment gestational age (21... | state + 3 * (agent_feats[7] * agent_feats[10] + agent_feats[8] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prioritize content suitable for both young and old audiences.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning agent.... | state * (agent_feats[7] + 2 * agent_feats[11] + 3 * agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.38%
Enrollment gestational age (11-20): 17.22%
Enrollment gestational age (21-30): 0.00%
Enrol... | state + 3 * (agent_feats[7] * agent_feats[10] + agent_feats[8] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prioritize content suitable for both young and old audiences.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning agent.... | (state + 1) * (agent_feats[7] or agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.69%
Enrollment gestational age (11-20): 17.41%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | state + 3 * (agent_feats[7] * agent_feats[10] + agent_feats[8] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 3 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Merge 'age' and 'education' profiles to prioritize vulnerable, low-income, young people.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Re... | state + state * ( (agent_feats[7] or agent_feats[8]) and (agent_feats[16] or agent_feats[17] or agent_feats[18]) and agent_feats[36]) + state * 2 * (agent_feats[36] and agent_feats[16])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment ges... | state + 3 * (agent_feats[7] and agent_feats[16]) + 2 * (agent_feats[7] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Merge 'age' and 'education' profiles to prioritize vulnerable, low-income, young people.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Re... | state + state * (agent_feats[16] or agent_feats[17] or agent_feats[18]) * (agent_feats[7] or agent_feats[8]) * (agent_feats[36] or agent_feats[37]) * 5
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollm... | state + 3 * (agent_feats[7] and agent_feats[16]) + 2 * (agent_feats[7] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Merge 'age' and 'education' profiles to prioritize vulnerable, low-income, young people.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Re... | state + 5 * (agent_feats[7] and agent_feats[16] and agent_feats[36]) + 2 * (agent_feats[8] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enro... | state + 3 * (agent_feats[7] and agent_feats[16]) + 2 * (agent_feats[7] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Reach out to NGO-affiliated individuals who indicated a preference for afternoon contact.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the R... | (state + 1) + (state + 1) * (agent_feats[28] and agent_feats[32])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.31%
Enrollment gestational age (11-20): 17.88%
Enrollment gestational age (21-30): 0.00%
Enrollme... | state + 5 * (agent_feats[32] and agent_feats[29])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Reach out to NGO-affiliated individuals who indicated a preference for afternoon contact.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the R... | state + 3 * agent_feats[28] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | state + 5 * (agent_feats[32] and agent_feats[29])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Reach out to NGO-affiliated individuals who indicated a preference for afternoon contact.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the R... | state + 5 * state * (agent_feats[28] and agent_feats[32])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.05%
Enrollment gestational age (11-20): 17.81%
Enrollment gestational age (21-30): 0.00%
Enrollment gesta... | state + 5 * (agent_feats[32] and agent_feats[29])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prioritize the top 12.5% of the income distribution and thoroughly analyze the tail end of the lowest income brackets.. The function should use 'state' (value is either 0,1) and features 'feats' (len... | state * (agent_feats[42] + 2*agent_feats[41] + 3*agent_feats[40])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.79%
Enrollment gestational age (11-20): 17.62%
Enrollment gestational age (21-30): 0.00%
Enrollme... | state + 5 * (agent_feats[36] * (1 - agent_feats[16]) + 3 * (agent_feats[17] + agent_feats[18] + agent_feats[19]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
En... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prioritize the top 12.5% of the income distribution and thoroughly analyze the tail end of the lowest income brackets.. The function should use 'state' (value is either 0,1) and features 'feats' (len... | state + state * (agent_feats[36] * 10 + (agent_feats[37] or agent_feats[38] )*5)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.75%
Enrollment gestational age (11-20): 17.46%
Enrollment gestational age (21-30):... | state + 5 * (agent_feats[36] * (1 - agent_feats[16]) + 3 * (agent_feats[17] + agent_feats[18] + agent_feats[19]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
En... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prioritize the top 12.5% of the income distribution and thoroughly analyze the tail end of the lowest income brackets.. The function should use 'state' (value is either 0,1) and features 'feats' (len... | state * (agent_feats[36] * 5 + agent_feats[37] * 3 + agent_feats[38] * 2 + agent_feats[41])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.36%
Enrollment gestational age (11-20): 17.49%
Enrollment gestational a... | state + 5 * (agent_feats[36] * (1 - agent_feats[16]) + 3 * (agent_feats[17] + agent_feats[18] + agent_feats[19]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
En... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Determine the income bracket range that encompasses the central 40% of the populace.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinfo... | state * (agent_feats[37] or agent_feats[38] or (2 * agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.68%
Enrollment gestational age (11-20): 17.67%
Enrollment gestational age (21-30): 0.00%
Enro... | state + 5 * (agent_feats[36] + 2 * agent_feats[37] + agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enro... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Determine the income bracket range that encompasses the central 40% of the populace.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinfo... | state + 3 * state * (agent_feats[37] or agent_feats[38]) + 2 * (agent_feats[6] + agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.48%
Enrollment gestational age (11-20): 17.68%
... | state + 5 * (agent_feats[36] + 2 * agent_feats[37] + agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enro... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Determine the income bracket range that encompasses the central 40% of the populace.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinfo... | state + state * (agent_feats[38] + agent_feats[39]) + 0.5*state*agent_feats[0]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 32.71%
Enrollment gestational age (11-20): 20.50%
Enrollment gestational age (21-30): 0... | state + 5 * (agent_feats[36] + 2 * agent_feats[37] + agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enro... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Establish the income bracket parameters for the median 40% of the population's earnings.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Re... | state * (agent_feats[37] or agent_feats[38] or (2 * agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.68%
Enrollment gestational age (11-20): 17.67%
Enrollment gestational age (21-30): 0.00%
Enro... | state + 5 * (agent_feats[36] + 2 * agent_feats[37] + agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enro... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Establish the income bracket parameters for the median 40% of the population's earnings.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Re... | state * (agent_feats[38] or agent_feats[39]) + 2 * (agent_feats[31] and agent_feats[10])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.79%
Enrollment gestational age (11-20): 17.33%
Enrollment gestational age ... | state + 5 * (agent_feats[36] + 2 * agent_feats[37] + agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enro... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Establish the income bracket parameters for the median 40% of the population's earnings.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Re... | state * (agent_feats[36] + agent_feats[37] + 2 * agent_feats[38] + agent_feats[39])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.02%
Enrollment gestational age (11-20): 17.86%
Enrollment gestational age (21-3... | state + 5 * (agent_feats[36] + 2 * agent_feats[37] + agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enro... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Specify the income bracket limits covering the central 40% of the population's income distribution.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to di... | state + state * (agent_feats[37] + agent_feats[38] + 2*agent_feats[39])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.07%
Enrollment gestational age (11-20): 17.84%
Enrollment gestational age (21-30): 0.00%
En... | state + 5 * (agent_feats[36] + 2 * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Specify the income bracket limits covering the central 40% of the population's income distribution.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to di... | state * (agent_feats[36] or agent_feats[37] or agent_feats[38] or agent_feats[39]) + 2 * state * (agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.04%
Enrollment gestational age... | state + 5 * (agent_feats[36] + 2 * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Specify the income bracket limits covering the central 40% of the population's income distribution.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to di... | state + state * (agent_feats[38] + agent_feats[39]) + 0.5*state*agent_feats[0]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 32.71%
Enrollment gestational age (11-20): 20.50%
Enrollment gestational age (21-30): 0... | state + 5 * (agent_feats[36] + 2 * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Identify the income bracket boundaries for the middle 40% of earners.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learnin... | state + state * (agent_feats[38] + agent_feats[39]) + 0.5 * state * (agent_feats[37] or agent_feats[40])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.85%
Enrollment gestational age (11-20): 17.66%
Enrollment... | state + 5 * (agent_feats[36] + agent_feats[37]) + 2 * (agent_feats[16] + agent_feats[17] + agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollme... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Identify the income bracket boundaries for the middle 40% of earners.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learnin... | state + 3 * state * (agent_feats[37] or agent_feats[38]) + 2 * (agent_feats[6] + agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.48%
Enrollment gestational age (11-20): 17.68%
... | state + 5 * (agent_feats[36] + agent_feats[37]) + 2 * (agent_feats[16] + agent_feats[17] + agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollme... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Identify the income bracket boundaries for the middle 40% of earners.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learnin... | state + 3 * state * (agent_feats[37] or agent_feats[38]) + 2 * (agent_feats[1] and agent_feats[6])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.48%
Enrollment gestational age (11-20): 17.68%
Enrollment gestat... | state + 5 * (agent_feats[36] + agent_feats[37]) + 2 * (agent_feats[16] + agent_feats[17] + agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollme... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prefer the income_bracket bounds for the middle 40% of the population.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learni... | state * (agent_feats[36] + 2 * agent_feats[37] + 3 * agent_feats[38])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.44%
Enrollment gestational age (11-20): 17.74%
Enrollment gestational age (21-30): 0.00%
Enro... | state + 5 * (agent_feats[36] + agent_feats[37]) + 2 * (agent_feats[16] + agent_feats[17] + agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollme... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prefer the income_bracket bounds for the middle 40% of the population.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learni... | state + state * (agent_feats[37] or agent_feats[38] or (agent_feats[36] and agent_feats[39]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.05%
Enrollment gestational age (11-20): 17.68%
Enrollment gestational... | state + 5 * (agent_feats[36] + agent_feats[37]) + 2 * (agent_feats[16] + agent_feats[17] + agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollme... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Prefer the income_bracket bounds for the middle 40% of the population.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learni... | state * (agent_feats[37] or agent_feats[38] or agent_feats[39]) + 2 * state * (agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.22%
Enrollment gestational age (11-20): 17.80%
En... | state + 5 * (agent_feats[36] + agent_feats[37]) + 2 * (agent_feats[16] + agent_feats[17] + agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollme... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: The focus should be on the upper 12.5% of the income range while giving special attention to the very bottom of the income distribution.. The function should use 'state' (value is either 0,1) and fea... | state * (2 * agent_feats[11] + 3 * agent_feats[42] + 4 * agent_feats[38] + agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.93%
Enrollment gestational age (11-20): 17.37%
Enrollment gestational a... | state + 5 * (agent_feats[36] and agent_feats[0]) + 3 * (agent_feats[36] and agent_feats[1])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | null |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: The focus should be on the upper 12.5% of the income range while giving special attention to the very bottom of the income distribution.. The function should use 'state' (value is either 0,1) and fea... | state + state * (agent_feats[36] * 4 + agent_feats[37] * 3 + agent_feats[38] * 2 + agent_feats[39] * agent_feats[41])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.27%
Enrollment gestational age (11-20): 17.40... | state + 5 * (agent_feats[36] and agent_feats[0]) + 3 * (agent_feats[36] and agent_feats[1])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: The focus should be on the upper 12.5% of the income range while giving special attention to the very bottom of the income distribution.. The function should use 'state' (value is either 0,1) and fea... | state + 5 * state * (agent_feats[42] and agent_feats[41]) + 2 * state * (agent_feats[40] or agent_feats[39])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.36%
Enrollment gestational age (11-20): 17.83%
Enrollm... | state + 5 * (agent_feats[36] and agent_feats[0]) + 3 * (agent_feats[36] and agent_feats[1])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -4 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: The lowest income_bracket distribution tail is very important, focus heavily on the top 12.5%.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct ... | state * (2 * agent_feats[11] + 3 * agent_feats[42] + 4 * agent_feats[38] + agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.93%
Enrollment gestational age (11-20): 17.37%
Enrollment gestational a... | state + 5 * (agent_feats[36] and agent_feats[16]) + 3 * (agent_feats[36] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: The lowest income_bracket distribution tail is very important, focus heavily on the top 12.5%.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct ... | state * (agent_feats[42] + 2*agent_feats[41] + 3*agent_feats[40])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.79%
Enrollment gestational age (11-20): 17.62%
Enrollment gestational age (21-30): 0.00%
Enrollme... | state + 5 * (agent_feats[36] and agent_feats[16]) + 3 * (agent_feats[36] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: The lowest income_bracket distribution tail is very important, focus heavily on the top 12.5%.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct ... | state + state * (8*agent_feats[42] + 4*agent_feats[41] + 2*agent_feats[40] + agent_feats[39])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.98%
Enrollment gestational age (11-20): 17.94%
Enrollment gestational... | state + 5 * (agent_feats[36] and agent_feats[16]) + 3 * (agent_feats[36] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational... | -3 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Heavily weight the examination toward the highest earners (top 12.5%), while critically assessing the characteristics of those in the lowest income brackets.. The function should use 'state' (value i... | state + state * (agent_feats[36] * 10 + agent_feats[37] * 5 + agent_feats[39] + agent_feats[42] * 3)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.55%
Enrollment gestational age (11-20): 17.46%
Enrollment gest... | state + 5 * (agent_feats[42] and (1 - agent_feats[36])) + 2 * (agent_feats[10] and (1 - agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Heavily weight the examination toward the highest earners (top 12.5%), while critically assessing the characteristics of those in the lowest income brackets.. The function should use 'state' (value i... | state + 4*state * (agent_feats[42] + 3*agent_feats[41] + 2*agent_feats[40])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.08%
Enrollment gestational age (11-20): 17.82%
Enrollment gestational age (21-30): 0.00... | state + 5 * (agent_feats[42] and (1 - agent_feats[36])) + 2 * (agent_feats[10] and (1 - agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Heavily weight the examination toward the highest earners (top 12.5%), while critically assessing the characteristics of those in the lowest income brackets.. The function should use 'state' (value i... | state * (1 + agent_feats[36]*(-10) + agent_feats[42]*5 + agent_feats[16] * 5 )
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.88%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0... | state + 5 * (agent_feats[42] and (1 - agent_feats[36])) + 2 * (agent_feats[10] and (1 - agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Concentrate your analysis on the wealthiest 12.5% of the income distribution, placing strong emphasis on the lowest income groups.. The function should use 'state' (value is either 0,1) and features ... | state + state * (7 * agent_feats[36] + 5*agent_feats[37] + 3 * agent_feats[38] + 2 * agent_feats[39] + 1*agent_feats[40] + 0.5 * agent_feats[41] + 0.1 * agent_feats[42])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-... | state + 5 * (agent_feats[42] and agent_feats[36]) + 10 * (1 - agent_feats[36]) * agent_feats[16]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestatio... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Concentrate your analysis on the wealthiest 12.5% of the income distribution, placing strong emphasis on the lowest income groups.. The function should use 'state' (value is either 0,1) and features ... | state * (2 * agent_feats[11] + 3 * agent_feats[42] + 4 * agent_feats[38] + agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.93%
Enrollment gestational age (11-20): 17.37%
Enrollment gestational a... | state + 5 * (agent_feats[42] and agent_feats[36]) + 10 * (1 - agent_feats[36]) * agent_feats[16]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestatio... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Concentrate your analysis on the wealthiest 12.5% of the income distribution, placing strong emphasis on the lowest income groups.. The function should use 'state' (value is either 0,1) and features ... | state + state * (agent_feats[36] * 50 + (agent_feats[42])) + (agent_feats[36] * 10 if agent_feats[16] or agent_feats[17] else 0)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.34%
Enrollment gestational age (11... | state + 5 * (agent_feats[42] and agent_feats[36]) + 10 * (1 - agent_feats[36]) * agent_feats[16]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestatio... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Craft material targeted towards children and senior citizens alike.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning ... | state * (agent_feats[7] + agent_feats[11] + 2 * agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollment gestational age (11-20): 17.10%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | state + 5 * (agent_feats[6] * agent_feats[10] + agent_feats[6] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Craft material targeted towards children and senior citizens alike.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning ... | state + state * (agent_feats[7] or agent_feats[11]) + 2 * state * (agent_feats[6] or agent_feats[42])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.31%
Enrollment gestational age (11-20): 18.04%
Enrollment ges... | state + 5 * (agent_feats[6] * agent_feats[10] + agent_feats[6] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Craft material targeted towards children and senior citizens alike.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning ... | state + 3 * (agent_feats[7] + agent_feats[11]) + 2 * (agent_feats[36] or agent_feats[42])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age... | state + 5 * (agent_feats[6] * agent_feats[10] + agent_feats[6] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Develop outputs keeping the interests of children and the elderly in mind.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Le... | (state + 1) * (agent_feats[7] or agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.69%
Enrollment gestational age (11-20): 17.41%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | state + 5 * (agent_feats[6] + agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.17%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Develop outputs keeping the interests of children and the elderly in mind.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Le... | state * (agent_feats[7] + agent_feats[11] + 2 * agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollment gestational age (11-20): 17.10%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | state + 5 * (agent_feats[6] + agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.17%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Develop outputs keeping the interests of children and the elderly in mind.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Le... | state * (agent_feats[7] + agent_feats[11] + 2 * agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollment gestational age (11-20): 17.10%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | state + 5 * (agent_feats[6] + agent_feats[10] + agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.17%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Center the investigation on young mothers, paying close attention to age demographics and financial struggles.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 a... | state * (agent_feats[7] and agent_feats[36]) + state * 0.5 * (agent_feats[8] and agent_feats[36]) + state * 0.25
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.53%
Enrollment gestational age (11-20): 17.42%
Enr... | state + 5 * (agent_feats[7] * (1 - agent_feats[36])) + 3 * (agent_feats[8] * (1 - agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gesta... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Center the investigation on young mothers, paying close attention to age demographics and financial struggles.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 a... | state + state * (agent_feats[7] or agent_feats[8]) * (agent_feats[36] or agent_feats[37]) + state * (agent_feats[16] or agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.72%
Enrollment gestational... | state + 5 * (agent_feats[7] * (1 - agent_feats[36])) + 3 * (agent_feats[8] * (1 - agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gesta... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Center the investigation on young mothers, paying close attention to age demographics and financial struggles.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 a... | state + state * (agent_feats[7] or agent_feats[8]) * (1 - (agent_feats[36] or agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.55%
Enrollment gestational age (11-20): 17.96%
Enrollment gestation... | state + 5 * (agent_feats[7] * (1 - agent_feats[36])) + 3 * (agent_feats[8] * (1 - agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gesta... | null |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus only on both the young and elderly.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning agent. Here is a descripti... | state + state * (agent_feats[7] or agent_feats[11]) + 2 * state * (agent_feats[6] or agent_feats[42])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.31%
Enrollment gestational age (11-20): 18.04%
Enrollment ges... | state + 5 * (agent_feats[7] * agent_feats[10] + agent_feats[7] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus only on both the young and elderly.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning agent. Here is a descripti... | state * (agent_feats[7] or agent_feats[10]) + state * (agent_feats[10])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.55%
Enrollment gestational age (11-20): 17.96%
Enrollment gestational age (21-30): 0.00%
En... | state + 5 * (agent_feats[7] * agent_feats[10] + agent_feats[7] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus only on both the young and elderly.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learning agent. Here is a descripti... | state * (agent_feats[7] + agent_feats[11] + 3 * agent_feats[42])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.65%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | state + 5 * (agent_feats[7] * agent_feats[10] + agent_feats[7] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Direct the AI's efforts towards the young and the elderly simultaneously.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | state * (agent_feats[7] + agent_feats[11] + 2 * (agent_feats[36] or agent_feats[42]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.34%
Enrollment gestational age (11-20): 17.22%
Enrollment gestational age (21... | state + 5 * (agent_feats[7] * agent_feats[10] + agent_feats[8] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Direct the AI's efforts towards the young and the elderly simultaneously.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | state + 2*state * (agent_feats[7] or agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.13%
Enrollment gestational age (11-20): 17.88%
Enrollment gestational age (21-30): 0.00%
Enrollment gestation... | state + 5 * (agent_feats[7] * agent_feats[10] + agent_feats[8] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Direct the AI's efforts towards the young and the elderly simultaneously.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | state * (agent_feats[7] + agent_feats[11] + 3 * agent_feats[42])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.65%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollmen... | state + 5 * (agent_feats[7] * agent_feats[10] + agent_feats[8] * agent_feats[11])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30)... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Target your analysis toward young mothers, considering their age alongside their financial circumstances.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state * (agent_feats[7] or agent_feats[8]) * (1 - agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.01%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollm... | state + 5 * (agent_feats[7] * agent_feats[36]) + 3 * (agent_feats[7] * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Target your analysis toward young mothers, considering their age alongside their financial circumstances.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state + state * (agent_feats[7] or agent_feats[8]) * (1 - (agent_feats[36] or agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.55%
Enrollment gestational age (11-20): 17.96%
Enrollment gestation... | state + 5 * (agent_feats[7] * agent_feats[36]) + 3 * (agent_feats[7] * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Target your analysis toward young mothers, considering their age alongside their financial circumstances.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state + state * (agent_feats[7] or agent_feats[8]) * (agent_feats[36] or agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age... | state + 5 * (agent_feats[7] * agent_feats[36]) + 3 * (agent_feats[7] * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Investigate the demographics of young mothers, concentrating on both their ages and their economic status.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array... | state * ( (agent_feats[7] or agent_feats[8]) and (agent_feats[36] or agent_feats[37]) ) + state
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestation... | state + 5 * (agent_feats[7] * agent_feats[36]) + 3 * (agent_feats[8] * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Investigate the demographics of young mothers, concentrating on both their ages and their economic status.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array... | state * (agent_feats[7] or agent_feats[8]) * (1 + 2 * (agent_feats[36] or agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 29.08%
Enrollment gestational age (11-20): 17.40%
Enrollment gestational a... | state + 5 * (agent_feats[7] * agent_feats[36]) + 3 * (agent_feats[8] * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Investigate the demographics of young mothers, concentrating on both their ages and their economic status.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array... | state + state * (agent_feats[7] or agent_feats[8]) * (agent_feats[36] or agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age... | state + 5 * (agent_feats[7] * agent_feats[36]) + 3 * (agent_feats[8] * agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Integrate 'age' and 'education' data, emphasizing support for impoverished youth with low incomes.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to dir... | state + (agent_feats[7] * (1 - agent_feats[16]) * (1 - agent_feats[36])) + (2 * state * agent_feats[16] * (1 - agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.17%
Enrollment gestational age (11... | state + 5 * (agent_feats[7] and agent_feats[16] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[17] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age ... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Integrate 'age' and 'education' data, emphasizing support for impoverished youth with low incomes.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to dir... | state + state * (agent_feats[16] or agent_feats[17] or agent_feats[18]) * (agent_feats[7] or agent_feats[8]) * (agent_feats[36] or agent_feats[37]) * 5
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollm... | state + 5 * (agent_feats[7] and agent_feats[16] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[17] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age ... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Integrate 'age' and 'education' data, emphasizing support for impoverished youth with low incomes.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to dir... | state + 5 * agent_feats[7] * (1 - agent_feats[16]) + 3 * (1 - agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0... | state + 5 * (agent_feats[7] and agent_feats[16] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[17] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age ... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Combine the distributions of 'age' and 'education' to heavily give precedence to low income impoverished youth.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 ... | state + 10 * agent_feats[7] * (1 - agent_feats[16]) * (1 - agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[7] and agent_feats[17]) + 2 * (agent_feats[7] and agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational a... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Combine the distributions of 'age' and 'education' to heavily give precedence to low income impoverished youth.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 ... | state + 5 * agent_feats[7] * (agent_feats[16] + agent_feats[17]) * (1 - agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age ... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[7] and agent_feats[17]) + 2 * (agent_feats[7] and agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Combine the distributions of 'age' and 'education' to heavily give precedence to low income impoverished youth.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 ... | state + state * ((agent_feats[7] or agent_feats[8]) and (agent_feats[16] or agent_feats[17] or agent_feats[18]) and agent_feats[36]) + state* (agent_feats[19] and agent_feats[37]) + state * agent_feats[0]*0.1
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestati... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[7] and agent_feats[17]) + 2 * (agent_feats[7] and agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational a... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Fuse the 'age' and 'education' distributions, specifically to elevate assistance for impoverished, low-income youth.. The function should use 'state' (value is either 0,1) and features 'feats' (lengt... | state + state * ((agent_feats[16] or agent_feats[17]) and (agent_feats[7] or agent_feats[8]) and agent_feats[36]) + 0.5 * state * ((agent_feats[16] or agent_feats[17] ) and (agent_feats[9] or agent_feats[18]) and agent_feats[36]) + 0.2 * state * ((agent_feats[18]) and (agent_feats[10]) and agent_feats[36])
And the co... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[7] and agent_feats[17]) + 2 * (agent_feats[7] and agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Fuse the 'age' and 'education' distributions, specifically to elevate assistance for impoverished, low-income youth.. The function should use 'state' (value is either 0,1) and features 'feats' (lengt... | state + 5 * (agent_feats[7] and agent_feats[16] and agent_feats[36]) + 2 * (agent_feats[8] and agent_feats[17])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enro... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[7] and agent_feats[17]) + 2 * (agent_feats[7] and agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Fuse the 'age' and 'education' distributions, specifically to elevate assistance for impoverished, low-income youth.. The function should use 'state' (value is either 0,1) and features 'feats' (lengt... | state + (agent_feats[7] * (1 - agent_feats[16]) * (1 - agent_feats[36])) + (2 * state * agent_feats[16] * (1 - agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.17%
Enrollment gestational age (11... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[7] and agent_feats[17]) + 2 * (agent_feats[7] and agent_feats[18])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational a... | 3 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Analyze young mothers, specifically examining the relationship between age and low income.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the ... | state * (agent_feats[7] or (2 * agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Analyze young mothers, specifically examining the relationship between age and low income.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the ... | state * ( (agent_feats[7] or agent_feats[8]) and (agent_feats[36] or agent_feats[37]) ) + state
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestation... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Analyze young mothers, specifically examining the relationship between age and low income.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the ... | state * (agent_feats[7] * 3 + agent_feats[8] * 2 + (1 - agent_feats[36]) * 4)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.50%
Enrollment gestational age (21-30): 0.... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Synthesize 'age' and 'education' metrics, focusing intensely on the needs of young people facing poverty.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state + state * (agent_feats[16] or agent_feats[17] or agent_feats[18]) * (agent_feats[7] or agent_feats[8]) * (agent_feats[36] or agent_feats[37]) * 5
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollm... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Synthesize 'age' and 'education' metrics, focusing intensely on the needs of young people facing poverty.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state + 5 * (agent_feats[7] and agent_feats[16]) + 3 * (agent_feats[36] or agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Synthesize 'age' and 'education' metrics, focusing intensely on the needs of young people facing poverty.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state + state * ( (agent_feats[7] or agent_feats[8]) and (agent_feats[16] or agent_feats[17] or agent_feats[18]) and agent_feats[36]) + state * (agent_feats[36] and (agent_feats[16] or agent_feats[17])) *2
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestationa... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[7] and agent_feats[37])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | state + state * (agent_feats[7] and agent_feats[36]) + 0.5 * state * (agent_feats[8] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.62%
Enrollmen... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[8] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | state * (agent_feats[7] or (2 * agent_feats[36]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[8] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | state * (agent_feats[7] or agent_feats[8]) * (1 + 2 * (agent_feats[36] or agent_feats[37]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 29.08%
Enrollment gestational age (11-20): 17.40%
Enrollment gestational a... | state + 5 * (agent_feats[7] and agent_feats[36]) + 3 * (agent_feats[8] and agent_feats[36])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Form the team of Hindi speakers to investigate beneficiary call consequences related to this.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct t... | state * agent_feats[12] * (agent_feats[8] + agent_feats[9] + 2 * agent_feats[10])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.26%
Enrollment gestational age (11-20): 16.97%
Enrollment gestational age (21-30)... | state + 5 * agent_feats[12] * (agent_feats[4] + agent_feats[5])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Form the team of Hindi speakers to investigate beneficiary call consequences related to this.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct t... | state * (agent_feats[12] ) + 1
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.19%
Enrollment gestational age (11-20): 17.12%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
C... | state + 5 * agent_feats[12] * (agent_feats[4] + agent_feats[5])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Form the team of Hindi speakers to investigate beneficiary call consequences related to this.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct t... | state * (agent_feats[12] * (agent_feats[1] + agent_feats[3] + 2 * agent_feats[6]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.09%
Enrollment gestational age (11-20): 17.18%
Enrollment gestational age (21-30... | state + 5 * agent_feats[12] * (agent_feats[4] + agent_feats[5])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Select solely those who speak Hindi to analyze how this would affect the calls made to our beneficiaries.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state * (agent_feats[12] * (agent_feats[0] + agent_feats[1] + agent_feats[2] + agent_feats[3] + agent_feats[4] + agent_feats[5]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 31.24%
Enrollment gestational age (1... | state + 5 * agent_feats[12] + 2 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | null |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Select solely those who speak Hindi to analyze how this would affect the calls made to our beneficiaries.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state * (agent_feats[12] * (agent_feats[0] + agent_feats[1] + agent_feats[2] + agent_feats[3] + agent_feats[4] + agent_feats[5]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 31.24%
Enrollment gestational age (1... | state + 5 * agent_feats[12] + 2 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | null |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Select solely those who speak Hindi to analyze how this would affect the calls made to our beneficiaries.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array)... | state * (agent_feats[12] * (agent_feats[1] + agent_feats[3] + 2 * agent_feats[6]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.09%
Enrollment gestational age (11-20): 17.18%
Enrollment gestational age (21-30... | state + 5 * agent_feats[12] + 2 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | null |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Appoint an exclusively Hindi-proficient group to analyze beneficiary call outcomes under this condition.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) ... | state * (agent_feats[12]) + 0.1
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.19%
Enrollment gestational age (11-20): 17.12%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
... | state + 5 * agent_feats[12] + 3 * agent_feats[16] + 2 * agent_feats[17]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
En... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Appoint an exclusively Hindi-proficient group to analyze beneficiary call outcomes under this condition.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) ... | state + 2*state * agent_feats[12]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.82%
Enrollment gestational age (11-20): 16.94%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00... | state + 5 * agent_feats[12] + 3 * agent_feats[16] + 2 * agent_feats[17]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
En... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Appoint an exclusively Hindi-proficient group to analyze beneficiary call outcomes under this condition.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) ... | state * (agent_feats[12] * (agent_feats[10] + agent_feats[11] + agent_feats[9]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.01%
Enrollment gestational age (11-20): 16.84%
Enrollment gestational age (21-30):... | state + 5 * agent_feats[12] + 3 * agent_feats[16] + 2 * agent_feats[17]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
En... | -1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Determine a Hindi-speaking team to examine how this choice modifies beneficiary calls.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Rein... | state * (agent_feats[12] * (agent_feats[0] + agent_feats[1] + agent_feats[2] + agent_feats[3] + agent_feats[4] + agent_feats[5]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 31.24%
Enrollment gestational age (1... | state + 5 * agent_feats[12] + 3 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Determine a Hindi-speaking team to examine how this choice modifies beneficiary calls.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Rein... | state * (agent_feats[12] and (agent_feats[10] + agent_feats[11] + agent_feats[9]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.01%
Enrollment gestational age (11-20): 16.84%
Enrollment gestational age (21-30... | state + 5 * agent_feats[12] + 3 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Determine a Hindi-speaking team to examine how this choice modifies beneficiary calls.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Rein... | (state + 1) * (agent_feats[12])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.19%
Enrollment gestational age (11-20): 17.12%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%... | state + 5 * agent_feats[12] + 3 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Identify, restricting solely to Hindi speakers, the team to assess call impact on beneficiaries.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direc... | state + state * agent_feats[12]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.08%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
... | state + 5 * agent_feats[12] + 3 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Identify, restricting solely to Hindi speakers, the team to assess call impact on beneficiaries.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direc... | state * (agent_feats[12] or 1)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
... | state + 5 * agent_feats[12] + 3 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | 1 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Identify, restricting solely to Hindi speakers, the team to assess call impact on beneficiaries.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direc... | state * (agent_feats[12] and (agent_feats[10] + 2*agent_feats[11] + 3*agent_feats[19]))
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.64%
Enrollment gestational age (11-20): 17.14%
Enrollment gestational age (... | state + 5 * agent_feats[12] + 3 * agent_feats[36]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Advantage those who prefer being called between 10:30am-12:30pm and are registered at an NGO.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct t... | state + state * (agent_feats[27] and agent_feats[32]) + 0.5*state * (agent_feats[27] or agent_feats[32])
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.63%
Enrollment gestational age (11-20): 17.40%
Enrollment ... | state + 5 * agent_feats[27] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Advantage those who prefer being called between 10:30am-12:30pm and are registered at an NGO.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct t... | state + state * (agent_feats[32] and agent_feats[27] * 3)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.10%
Enrollment gestational age (11-20): 17.79%
Enrollment gestational age (21-30): 0.00%
Enrollment gesta... | state + 5 * agent_feats[27] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Advantage those who prefer being called between 10:30am-12:30pm and are registered at an NGO.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct t... | state + state * (agent_feats[32] and agent_feats[27] * 3)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.10%
Enrollment gestational age (11-20): 17.79%
Enrollment gestational age (21-30): 0.00%
Enrollment gesta... | state + 5 * agent_feats[27] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Contact preferred individuals from NGOs between 10:30 am and 12:30 pm.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learni... | state + state * (agent_feats[27] and agent_feats[32]) * 2
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.33%
Enrollment gestational age (11-20): 17.61%
Enrollment gestational age (21-30): 0.00%
Enrollment gest... | state + 5 * agent_feats[32] * agent_feats[26] + 2 * agent_feats[31] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21... | -2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Contact preferred individuals from NGOs between 10:30 am and 12:30 pm.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learni... | state + 5 * agent_feats[32] * agent_feats[27]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | state + 5 * agent_feats[32] * agent_feats[26] + 2 * agent_feats[31] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21... | 2 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Contact preferred individuals from NGOs between 10:30 am and 12:30 pm.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Learni... | state + 5 * agent_feats[32] * agent_feats[27]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | state + 5 * agent_feats[32] * agent_feats[26] + 2 * agent_feats[31] * agent_feats[32]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21... | -3 |
[INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Call those connected with an NGO who would prefer a phone call between 10:30 and 12:30.. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Rei... | state * (agent_feats[32] and agent_feats[27] * 3)
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.39%
Enrollment gestational age (11-20): 17.28%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational a... | state + 5 * agent_feats[32] * agent_feats[27]
And the corresponding output reward distribution from this reward function is:
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.18%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (... | 2 |
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