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We compare different Bayesian methods for representing an RL agent's uncertainty about cumulative rewards, including our own approach based on moment matching across the Bellman equations.
The main features of stratismarkou/sample-efficient-bayesian-rl are: Reinforcement Learning Environments, World Models.
Projects with overlapping indexed features include: farama-foundation/metaworld — Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning. minerllabs/minerl — Python package providing easy to use Gym environments and data access for training agents in Minecraft. aravindr93/mjrl — This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo. danijar/crafter — Status: Stable release. maximecb/gym-minigrid — Simple and easily configurable grid world environments for reinforcement learning. openai/gym — Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and…
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo.
Simple and easily configurable grid world environments for reinforcement learning
