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Data-Efficient Hierarchical Reinforcement Learning

initial_h 2022-05-30 23:43 阅读:89 评论:0 推荐:0 编辑

An Investigation of Model-Free Planning

initial_h 2022-05-25 23:32 阅读:31 评论:0 推荐:0 编辑

A0C: Alpha Zero in Continuous Action Space

initial_h 2022-05-23 23:05 阅读:235 评论:0 推荐:0 编辑

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

initial_h 2022-05-20 23:43 阅读:69 评论:0 推荐:0 编辑

Discovering symbolic policies with deep reinforcement learning

initial_h 2022-05-18 23:52 阅读:133 评论:0 推荐:0 编辑

Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

initial_h 2022-05-15 23:07 阅读:36 评论:0 推荐:0 编辑

Planning to Explore via Self-Supervised World Models

initial_h 2022-05-13 22:54 阅读:137 评论:0 推荐:0 编辑

EXPLORATION BY RANDOM NETWORK DISTILLATION

initial_h 2022-05-13 22:50 阅读:271 评论:0 推荐:0 编辑

NEVER GIVE UP: LEARNING DIRECTED EXPLORATION STRATEGIES

initial_h 2022-05-08 23:59 阅读:237 评论:0 推荐:0 编辑

Discovering and Achieving Goals via World Models

initial_h 2022-05-04 22:23 阅读:122 评论:0 推荐:0 编辑

Agent57: Outperforming the Atari Human Benchmark

initial_h 2022-05-02 23:08 阅读:401 评论:0 推荐:0 编辑

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

initial_h 2022-04-29 23:23 阅读:163 评论:0 推荐:0 编辑

Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning

initial_h 2022-04-27 23:44 阅读:41 评论:0 推荐:0 编辑

Application of MCTS in Atari Black-box Planning

initial_h 2022-04-27 23:40 阅读:36 评论:0 推荐:0 编辑

Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

initial_h 2022-04-27 23:34 阅读:116 评论:0 推荐:0 编辑