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

NLPGym - A toolkit to develop RL agents to solve NLP tasks.

Tensorflow 2 Reinforcement Learning Cookbook, published by Packt

Train a tic-tac-toe agent using reinforcement learning.

RL-Toolkit: A Research Framework for Robotics

Pytorch Implementation of RL algorithms

Boid flock multi-agent RL training environment implemented in JAX

Implementation of Continuous Control RL Algorithms

Mobile Apps (Android) as Environment for Reinforcement Learning Agents

Pytorch Implementation of RL algorithms

dITC through RL Code Foundation

Custom Reinforcement Learning Agents

This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.

A reinforcement learning agent navigating the OpenAI's FrozenLake environment

Our project focuses on the problem of generating synthetic levels of a game such that the levels can be used to learn an optimal policy for playing the game. Given a few pre-existing game levels we want to use deep generative models (like GANs) to generate new additional game levels. We will then train an RL agent on these levels to learn a gene…

Collect more gift than an AI opponent in this fast-paced Christmas-themed game.

A general-purpose remote environment for training RL agents.

This project trains and evaluates a Proximal Policy Optimization (PPO) agent to play the Atari game Atlantis using Stable Baselines3. The agent is trained with a Convolutional Neural Network (CNN) policy and evaluated for its performance in the game. It includes scripts for training, evaluating, and real-time gameplay rendering.

First version of social 2-step task with shocks.

Some Reinforcement Learning in Python. Especially how to get the feature for linear function approximation.