Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

Proximal Policy Optimization (PPO) algorithm using PyTorch to train an agent for a rocket landing task in a custom environment

PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.

Implementation of PPO Lagrangian in PyTorch

PyTorch implementation of GAIL and PPO reinforcement learning algorithms

A Torch Based RL Framework for Rapid Prototyping of Research Papers

Worst-case MSE Minimization for RIS-assisted mmWave MU-MISO Systems with Hardware Impairments and CSI Imperfection

GAIL learning to imitate PPO playing CartPole.

TradeWhisperer is a sophisticated cryptocurrency trading bot that leverages advanced Reinforcement Learning techniques, specifically the Proximal Policy Optimization (PPO) algorithm, to navigate the complex world of crypto markets. Built with a focus on adaptability and risk management, this bot combines technical analysis with machine learning.

Multi agent PPO implementation in Pytorch for Unity ML Agents environments.

Automated & super fast PyTorch deep reinforcement learning platform for autonomous driving

AI agent learns to walk, run, hop and crawl with out any given data using proximal policy optimisation.

This is the Tic-Tac-Toe game made with Python using the PyGame library and the Gym library to implement the AI with Reinforcement Learning

DRL-Base-EMS for HEVs

Agent trained to play battleship using reinforcement learning (PPO) and openAI gym

Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.

Proximal Policy Optimization method in Pytorch

Repository of Various Test & Implementation of RL

Solving pursuit-evasion problems on graphs using Reinfocement Learning and GNNs