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64ea2b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import matplotlib.pyplot as plt
import numpy as np
from agents.dqn_agent import dqn_agent, QNetwork
from env.tasks import task_easy, task_medium, task_hard
from agents.greedy_agent import greedy_agent
from agents.q_learning_agent import q_learning_agent
from agents.baseline_agent import llm_agent
def run_episode(env, agent):
state = env.reset()
total_reward = 0
done = False
while not done:
action = agent(state)
state, reward, done, _ = env.step(action)
total_reward += reward.value
return total_reward
def plot_results():
# Initialize DQN model
input_dim = 3 + 1 + 1 + 3 + 1 + 1 # interest_vector(3) + fatigue + session_time + topic_vector(3) + quality + length
dqn_model = QNetwork(input_dim)
tasks = [
("Easy", task_easy()),
("Medium", task_medium()),
("Hard", task_hard()),
]
agents = [
("Greedy", greedy_agent),
("Q-Learning", q_learning_agent),
("DQN", lambda state: dqn_agent(state, dqn_model)),
("Baseline", llm_agent),
]
results = {}
for task_name, env in tasks:
results[task_name] = {}
for agent_name, agent in agents:
reward = run_episode(env, agent)
results[task_name][agent_name] = reward
# Plotting
task_names = list(results.keys())
agent_names = list(results[task_names[0]].keys())
num_tasks = len(task_names)
num_agents = len(agent_names)
x = np.arange(num_tasks)
width = 0.25
fig, ax = plt.subplots(figsize=(10, 6))
for i, agent_name in enumerate(agent_names):
rewards = [results[task][agent_name] for task in task_names]
ax.bar(x + i * width, rewards, width, label=agent_name)
ax.set_xlabel('Task Difficulty')
ax.set_ylabel('Total Reward')
ax.set_title('Agent Performance Across Tasks')
ax.set_xticks(x + width)
ax.set_xticklabels(task_names)
ax.legend()
plt.tight_layout()
plt.savefig('agent_performance.png')
plt.show()
if __name__ == "__main__":
plot_results() |