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...
| Huvudupphovsmän: | , , , |
|---|---|
| Materialtyp: | Conference item |
| Publicerad: |
International Conference on Learning Representations
2018
|