Meta-World+: An Improved, Standardized, RL Benchmark
원문 초록
Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction however, there have been numerous undocumented changes which inhibit a fair comparison of algorithms. This work strives to disambiguate these results from the literature, while also leveraging the past versions of Meta-World to provide insights into multi-task and meta-reinforcement learning benchmark design. Through this process we release a new open-source version of Meta-World (https://github.com/Farama-Foundation/Metaworld/) that has full reproducibility of past results, is more technically ergonomic, and gives users more control over the tasks that are included in a task set.
함께 살펴볼 논문
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Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
Scaling Algorithm Distillation for Continuous Control with Mamba
Meta-reinforcement learning with minimum attention
Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing
Fully Offline Reinforcement Learning
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.