TreeQN and ATreeC: differentiable tree planning for deep reinforcement learning
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where tran...
| 主要な著者: | , , , |
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| フォーマット: | Conference item |
| 出版事項: |
International Conference on Learning Representations
2018
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