A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

Curiosity-driven Exploration by Self-supervised Prediction for Street Fighter III Third Strike

Gym environments and agents for autonomous driving.

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

TensorFlow A2C to solve Acrobot, with synchronized parallel environments

Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"

An easy-to-use reinforcement learning library for research and education.

The MAGICAL benchmark suite for robust imitation learning (NeurIPS 2020)

Robotic simulation in Unity with ROS integration.

pytorch implementation of Curiosity-driven Exploration by Self-supervised Prediction

Reinforcement Learning for Super Mario Bros using A3C on GPU

No description or website provided.

A simple, continuous-control environment for OpenAI Gym

Minimal implementations of reinforcement learning algorithms by Tensorflow

Deep Reinforcement Learning in Autonomous Driving: the A3C algorithm used to make a car learn to drive in TORCS; Python 3.5, Tensorflow, tensorboard, numpy, gym-torcs, ubuntu, latex

RealAnt robot platform for low-cost, real-world reinforcement learning

React Native App using react, redux, sagas, hooks, react navigation v5

Simulator for Reinforcement Learning and AI. 2D environments with physics and interactive entities. Agents with rich sensors and actuators.

Modelica models integration with Open AI Gym

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