Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

[ICLR 2023] Come & try Decision-Intelligence version of "Agar"! Gobigger could also help you with multi-agent decision intelligence study.

[NeurIPS 2022] 🛒WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Baba Is You simulator using C++ with some reinforcement learning

YAWNING TITAN is an abstract, graph based cyber-security simulation environment that supports the training of intelligent agents for autonomous cyber operations.

Reproduced the popular Facebook game -- Tetris Battle! Also provided openAI environments.

Unrailed! simulator using C++ with some reinforcement learning and Unrailed! AI using Python with OpenCV

Xenoverse is a collection of randomized RL, Language, and general-purpose simulation environments, designed for training General-Purpose Learning Agents (GLAs).

Flappy Bird RL environment with Segmentation

A Grid Based RL Environment & Implementaions of few Deep-RL Algorithms.

This fork adds multi-agent social dilemma environments in Gym: CoinGame, (Iterated) Prisonner Dilemma, Stag Hunt, Chicken, Matching Pennies. Gym: A toolkit for developing and comparing reinforcement learning algorithms.

Reinforcement Learning agent for testing registration form behavior in sandbox. 🧪 This project was developed as part of a course assignment focused on practical reinforcement learning applications in a simulated environment.

Implement of Behavior Cloning (BC) and Conservative Q-Learning (CQL) algorithms for training reinforcement learning models using a dataset of state-action pairs. It provides an environment for experimenting with these algorithms, comparing their performance, and analyzing the effects of different parameters and dataset variations on training result