Open-source alternatives to Gym4ReaL

A flexible and efficient training framework for large-scale alignment tasks

ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques. The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement

DAMO-ConvAI: The official repository which contains the codebase for Alibaba DAMO Conversational AI.

This repository contains the implementation of DISCERN in Python. You can download the manuscript from my website or arXiv.

⚡️A Blazing-Fast Python Library for Ranking Evaluation, Comparison, and Fusion 🐍

This repository provides supplementary material for our paper Constitutional AI: Harmlessness from AI Feedback.

A short and easy implementation of Quantile Regression DQN | Distributional Reinforcement Learning

Reinforcement Learning in PyTorch

Reinforcement Learning in Go

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

TensorFlow implementation of Deep Reinforcement Learning papers

Accelerated RL

|tests| |pypi| |docs| |License|

A library with extensible implementations of DPO, KTO, PPO, ORPO, and other human-aware loss functions (HALOs).

Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using reinforcement learning. It serves as a spatial AI training simulator where agents can be evaluated through navigation and puzzle-solving tasks. The environment allows for the definition of complex layouts and task behaviors through external scripting, enabling the generation of specific challenges for AI research. It supports both automated training via standard API bindings and manual agent control to validate simulation dynamics. The system utilizes a grid-based spatial represen

Mctx is a library with a JAX-native implementation of Monte Carlo tree search (MCTS) algorithms such as AlphaZero, MuZero, and Gumbel MuZero. For computation speed up, the implementation fully supports JIT-compilation. Search algorithms in Mctx are defined for and operate on batches of inputs,…

pysc2 is a Python interface and simulation framework that connects the StarCraft II game engine to machine learning agents. It acts as an API wrapper that exposes game internals as a set of observations and actions, providing a reinforcement learning environment for research and training. The framework includes tools for game replay analysis to extract data and sequences of actions from recorded matches for predictive modeling. It also provides an agent simulation environment to run and evaluate the performance of single or competing artificial intelligence agents. The system handles game ma

RLax (pronounced "relax") is a library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents. Full documentation can be found at rlax.readthedocs.io.

TensorFlow Reinforcement Learning

This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods. The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approxima

Project Page Paper