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A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.
An environment of the board game Go using OpenAI's Gym API
Reinforcement Learning Agents Trained in the CARLA Simulator
A high-fidelity simulation backend for Franka robots that implements the complete libfranka API. Develop and test your robot control applications with identical code for both simulation and hardware. Supports all joint control modes (position, velocity, torque) with realistic physics powered by the Genesis engine.
OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone.
Code for reproducing results in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"
A collection of several Deep Reinforcement Learning techniques (Deep Q Learning, Policy Gradients, ...), gets updated over time.
A graphical interface for reinforcement learning and gym-based environments.
General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems
Beer Game implemented as an OpenAI gym environment.
Implementations of various RL and Deep RL algorithms in TensorFlow, PyTorch and Keras.
An infrastructure built using PyGame and OpenAI Gymnasium used to train robots within a social navigation context with a wide range of human motion models to simulate crowds of pedestrians.
OpenAI Gym environment designed for training RL agents to balance double CartPole.
👾 RL agent that completes the Battle City arcade game
Simple PyTorch implementation of Deep Q-learning Algorithm to play Lunar Lander.
Simple Minimalistic Gridworld Environment for OpenAI Gym (Simple-MiniGrid)
💳 Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.
Code for generating data in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"
Training material - An RL playground to test Karpathy's great tutorial on Policy Gradients.
An implementation from the state-of-the-art family of reinforcement learning algorithms Proximal Policy Optimization using normalized Generalized Advantage Estimation and optional batch mode training. The loss function incorporates an entropy bonus.
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