Open-source alternatives to Mctx

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 replica of the AlphaZero methodology for deep reinforcement learning in Python

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

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

Tensorflow Keras OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"

Gymnasium-based benchmarking suite for testing RL algorithms on real-world scenarios

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

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