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

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.

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.

OpenAI Gym environment designed for training RL agents to balance double CartPole.

Simple Minimalistic Gridworld Environment for OpenAI Gym (Simple-MiniGrid)

Training material - An RL playground to test Karpathy's great tutorial on Policy Gradients.

👾 RL agent that completes the Battle City arcade game

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.

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 bring CartPole upright and its further balancing.

Deep reinforcement learning solution for some open AI gym environments.

Using Reinforcement Learning to solve Maze Navigation, Acrobot, Mountain Car

Asteroids evasion using OpenAI's gym Reinforcement Learning (RL) package - M.Sc. Thesis in Computer Science, Ben Gurion University Ben Gurion University of the Negev, Israe

Python script to balance Pendulum from open ai gym using Q-Learning and Double Q-Learning