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.
Code for reproducing results in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"
OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone.
A graphical interface for reinforcement learning and gym-based environments.
A collection of several Deep Reinforcement Learning techniques (Deep Q Learning, Policy Gradients, ...), gets updated over time.
General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems
Implementations of various RL and Deep RL algorithms in TensorFlow, PyTorch and Keras.
Beer Game implemented as an OpenAI gym environment.
Simple PyTorch implementation of Deep Q-learning Algorithm to play Lunar Lander.
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.
Simple Minimalistic Gridworld Environment for OpenAI Gym (Simple-MiniGrid)
Code for generating data in ICML 2020 paper "PackIt: A Virtual Environment for Geometric Planning"
👾 RL agent that completes the Battle City arcade game
Training material - An RL playground to test Karpathy's great tutorial on Policy Gradients.
Solution to the Deep RL Bootcamp labs from UC Berkeley
Course work of Reinforcement-Learning-CS6700
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