#python #atari #deepmind_lab #gcp #google_research_football #impala #r2d2 #rl #tf2

https://github.com/google-research/seed_rl

#python #alphago #alphazero #deep_learning #deep_reinforcement_learning #gym #machine_learning #mcts #model_based_rl #monte_carlo_tree_search #muzero #muzero_general #neural_network #python3 #pytorch #reinforcement_learning #residual_network #rl #self_learning #tensorboard

https://github.com/werner-duvaud/muzero-general

#python #deep_reinforcement_learning #gym #hyperparameter_optimization #hyperparameter_search #hyperparameter_tuning #lab #openai #optimization #pybullet #pybullet_environments #pytorch #reinforcement_learning #rl #robotics #sde #stable_baselines #tuning_hyperparameters

https://github.com/DLR-RM/rl-baselines3-zoo

#python #ai #control #decision_making #distributed_computing #machine_learning #marl #model_based_reinforcement_learning #multi_agent_reinforcement_learning #pytorch #reinforcement_learning #rl #robotics #torch

https://github.com/pytorch/rl

#python #agent #agentic_ai #grpo #kimi_ai #llms #lora #qwen #qwen3 #reinforcement_learning #rl

ART is a tool that helps you train smart agents for real-world tasks using reinforcement learning, especially with the GRPO method. The standout feature is RULER, which lets you skip the hard work of designing reward functions by using a large language model to automatically score how well your agent is doing—just describe your task, and RULER takes care of the rest. This makes building and improving agents much faster and easier, works for any task, and often performs as well as or better than hand-crafted rewards. You can install ART with a simple command and start training agents right away, even on your own computer or with cloud resources.

https://github.com/OpenPipe/ART

#python #gym #gym_environment #reinforcement_learning #reinforcement_learning_agent #reinforcement_learning_environments #rl_environment #rl_training

NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior.

https://github.com/NVIDIA-NeMo/Gym