Ray RLlib
A distributed reinforcement learning library within the Ray ecosystem, supporting large-scale parallel training and implementations of multiple algorithms, intended for industrial-grade RL applications.
Timeline (last 90 days)
ML-Agents works as follows: on the Unity side, observation, action, and reward are defined through Agent scripts, and communication with an external Python training process is done via gRPC; on the Python side, reference implementations of algorithms such as PPO and SAC are provided (based on PyTorch), and Stable-Baselines3 or Ray RLlib can also be connected through the mlagents-envs interface.
Ray RLlib 是面向分布式场景的工业级强化学习框架，支持多智能体训练和大规模并行环境采样
All Facts (2)
ML-Agents works as follows: on the Unity side, observation, action, and reward are defined through Agent scripts, and communication with an external Python training process is done via gRPC; on the Python side, reference implementations of algorithms such as PPO and SAC are provided (based on PyTorch), and Stable-Baselines3 or Ray RLlib can also be connected through the mlagents-envs interface.
50%UnverifiedRay RLlib 是面向分布式场景的工业级强化学习框架，支持多智能体训练和大规模并行环境采样
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