当前标签：model-based

MODEL-AUGMENTED PRIORITIZED EXPERIENCE REPLAY

initial_h 2023-07-03 11:25 阅读:81 评论:0 推荐:0

Muesli: Combining Improvements in Policy Optimization

initial_h 2023-06-02 22:36 阅读:44 评论:0 推荐:0

POLICY IMPROVEMENT BY PLANNING WITH GUMBEL

initial_h 2023-05-27 21:11 阅读:252 评论:0 推荐:0

DYNAMICS-AWARE UNSUPERVISED DISCOVERY OF SKILLS

initial_h 2023-05-09 22:41 阅读:103 评论:0 推荐:0

Deep Dynamics Models for Learning Dexterous Manipulation

initial_h 2023-04-30 13:34 阅读:118 评论:0 推荐:0

EXPLORING MODEL-BASED PLANNING WITH POLICY NETWORKS

initial_h 2023-04-27 23:02 阅读:86 评论:0 推荐:0

Learning Off-Policy with Online Planning

initial_h 2023-04-23 12:56 阅读:56 评论:0 推荐:0

The Second Type of Uncertainty in Monte Carlo Tree Search

initial_h 2023-04-20 23:14 阅读:75 评论:0 推荐:0

Value targets in off-policy AlphaZero: a new greedy backup

initial_h 2023-04-16 10:23 阅读:45 评论:0 推荐:1

Visualizing MuZero Models

initial_h 2023-04-12 22:29 阅读:75 评论:0 推荐:0

The Predictron: End-To-End Learning and Planning

initial_h 2023-04-03 10:48 阅读:59 评论:0 推荐:0

Sample-Based Learning and Search with Permanent and Transient Memories

initial_h 2023-03-30 12:02 阅读:45 评论:0 推荐:0

Learning model-based planning from scratch

initial_h 2023-03-27 23:24 阅读:88 评论:0 推荐:0

COMBINING Q-LEARNING AND SEARCH WITH AMORTIZED VALUE ESTIMATES

initial_h 2023-03-06 01:03 阅读:124 评论:0 推荐:0

Monte-Carlo tree search as regularized policy optimization

initial_h 2023-02-25 23:04 阅读:178 评论:0 推荐:0

PROCEDURAL GENERALIZATION BY PLANNING WITH SELF-SUPERVISED WORLD MODELS

initial_h 2022-11-25 12:28 阅读:61 评论:0 推荐:0

A0C: Alpha Zero in Continuous Action Space

initial_h 2022-05-23 23:05 阅读:411 评论:0 推荐:0

Planning to Explore via Self-Supervised World Models

initial_h 2022-05-13 22:54 阅读:245 评论:0 推荐:0

Discovering and Achieving Goals via World Models

initial_h 2022-05-04 22:23 阅读:201 评论:0 推荐:0

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

initial_h 2022-04-27 23:44 阅读:77 评论:0 推荐:0