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rl_games is a high-performance reinforcement learning framework optimized for GPU-based training, particularly in environments like robotics and continuous control tasks. It supports advanced algorithms and is built with PyTorch.
Features
- Implements high-performance RL algorithms optimized for GPUs
- Supports Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC)
- Designed for robotics, continuous control, and physics-based simulations
- Compatible with Isaac Gym for large-scale parallelized RL
- Includes training scripts for efficient experimentation
Project Samples
Categories
Reinforcement Learning FrameworksLicense
MIT LicenseFollow RL Games
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