arXiv:1705.03562
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning
원문 초록
We present a new deep meta reinforcement learner, which we call Deep Episodic Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity metric for a non-parametric model-based reinforcement learning algorithm. Our model is trained end-to-end via back-propagation. Despite being trained using the model-free Q-learning objective, we show that DEVI's model-based internal structure provides `one-shot' transfer to changes in reward and transition structure, even for tasks with very high-dimensional state spaces.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Task Independent Capsule-Based Agents for Deep Q-Learning
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better Performance
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
Deep Reinforcement Learning Based Power Allocation for D2D Network
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.