{"library":"rsl-rl-lib","title":"RSL RL Lib","description":"Fast and simple reinforcement learning algorithms (PPO) implemented in PyTorch, primarily designed for robotics applications like Isaac Lab. Current version 5.2.0, released April 2025. Active development with frequent releases.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install rsl-rl-lib"],"cli":null},"imports":["from rsl_rl.runners import OnPolicyRunner","from rsl_rl.algorithms import PPO","from rsl_rl.models import ActorCritic"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import gym\nimport torch\nfrom rsl_rl.runners import OnPolicyRunner\nfrom rsl_rl.algorithms import PPO\nfrom rsl_rl.modules import ActorCritic\n\n# Initialize environment (example using gym)\nenv = gym.make('CartPole-v1')\n\n# Setup model and algorithm\nactor_critic = ActorCritic(\n    num_actor_obs=env.observation_space.shape[0],\n    num_critic_obs=env.observation_space.shape[0],\n    num_actions=env.action_space.shape[0],\n).to('cuda')\nalgo = PPO(actor_critic=actor_critic, num_learning_epochs=5)\n\n# Create runner and train\nrunner = OnPolicyRunner(env, algo, device='cuda')\nrunner.learn(num_learning_iterations=100, init_at_random_ep_len=True)\nprint('Training complete!')","lang":"python","description":"Basic training loop using OnPolicyRunner","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}