| 2: | 1: Input: K ∈ N+ copies of independently initialized Q*-functions {Qk}k=1· Let B be a replay buffer storing transitions for training |
| for each episode do do |
| 3: 4: | Obtain initial state from environment so |
| 5: | for step t = 1,... until end of episode do |
| 6: | Pick an action aording toat = MajorityVote({argmaxq Qk(st,α)}-1) |
| 7: | Execute at. Receive state St+1 and reward rt from the environment |
| 8: | Add (st, at, rt, St+1) to replay buffer B |
| 9: | At learning interval, sample random minibatch and update {Qk } |
| 10: 11: end for | end for |
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| Let B be a replay buffer storing experience for training. |
| 3: for each episode do |
| Obtain initial state from environment so |
| for step t = 1,... until end of episode do |
| Pick an action according to at ∈ argmaxa {μ(St,a) + λ · δ(st, a)} |
| 6: 7: Receive state St+1 and reward rt from environment, having taken action ät |
| 8: Add (St,at, rt, St+1) to replay buffer B |
| 9: At learning interval, sample random minibatch and update {Qk} |
| 10: end for |
| 11: end for |
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| hyperparameter total training frames | 40 million | descriptions | Length of training for each game. |
| minibatch size | 32 |
| replay buffer size | 1000000 | parameter update. | The number of most recent frames |
| agent history length | 4 |
| | length. | concatenated as input to the Q net- work. Total number of iterations = total training frames /agent history |
| target network update10000 frequency | |
| discount factor | 0.99 |
| action repeat | 4 |
| update frequency 4 | |
| optimizer | Adam | Optimizer for parameter updates. |
| β1 0.9 | Adam optimizer parameter. |
| β | 0.99 | Adam optimizer parameter. |
| E 10-4 | | Adam optimizer parameter. |
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| 2: Let B be a replay buffer storing experience for training. for each episode do | |
| 3: | |
| 4: | Obtain initial state from environment so |
| 5: | for step t = 1,... until end of episode do |
| 6: | Pick an action according to at ∈ argmaxa{μ(st,a) + λ ·δ(st,a)} |
| 7: | Receive state St+1 and reward rt from environment, having taken action at |
| 8: | Calculate exploration bonus br(st） according to (12) |
| 9: | Add (St,at,rt + ρ : br(St), St+1) to replay buffer B |
| 10: | At learning interval, sample random minibatch and update {Qk} |
| 11: end for 12: end for |
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