3 Repos
Awesome GitHub RepositoriesOffline Reinforcement Learning
Training reinforcement learning policies using pre-collected datasets without active environment interaction.
Distinct from Dataset-Driven Training: Distinct from general dataset-driven training as it specifically implements offline RL algorithms.
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Awesome Offline Reinforcement Learning GitHub Repositories
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- morvanzhou/reinforcement-learning-with-tensorflow
MorvanZhou/Reinforcement-learning-with-tensorflow
9,464Auf GitHub ansehenThis project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor
Supports offline reinforcement learning by training policies from pre-collected datasets.
Pythona3cactor-criticasynchronous-advantage-actor-critic - facebookresearch/horizon
facebookresearch/Horizon
3,703Auf GitHub ansehenHorizon is a reinforcement learning platform designed for training, evaluating, and deploying agents and contextual bandits using historical data. It serves as an off-policy engine and offline policy evaluation tool, allowing decision-making policies to be optimized and tested without the need for a live simulator. The framework specializes in recommendation system optimization, specifically using slating-based reinforcement learning to optimize the ordering and sequencing of multiple recommendations. It also functions as a contextual bandit framework that manages the balance between explorat
Implements a framework for training and optimizing reinforcement learning agents using pre-collected historical datasets.
Python - rlinf/rlinf
RLinf/RLinf
2,502Auf GitHub ansehenRLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
Creates reinforcement learning policies using pre-collected datasets without interacting with the live environment.
Pythonagentic-aiembodied-aireinforcement-learning
