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Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.
| Date | Stars |
|---|---|
| 2026-07-31 | 301 |
| 2026-08-01 | 301 |
| 2026-08-02 | 301 |
| 2026-08-06 | 301 |
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# MetaGym MetaGym provides abundant environments for benchmarking Reinforcement Learning and Meta Reinforcement Learning # Environments Updating - [LiftSim](metagym/liftsim)：Simulator for Evelvator Dispatching (Sep, 2019) - [Quadrotor](metagym/quadrotor): 3D Quadrotor simulator for different tasks (Mar, 2020) - [Quadrupedal](metagym/quadrupedal): Quadrupedal robot adapting to different terrains (Seq, 2021) - [MetaMaze](metagym/metamaze): 2D/3D maze generators for task generalization (Oct, 2021) - [MetaLocomotion](metagym/metalocomotion): Locomotion simulator with diverse geometries (June, 2022) - [MetaLM](metagym/metalm): Meta language model dataset (Dec, 2022) - [Bandits](metagym/bandits): Bandits task generalization (Dec, 2022)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:be67ef9d2ec8e960, topic:reinforcement-learning, desc:reinforcement learning