PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

A curated list of awesome frameworks, libraries, tools, environments, tutorials, research papers, and resources for reinforcement learning (RL). This list covers fundamental concepts, advanced algorithms, applications, and popular frameworks for building RL models.

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Popular Deep RL algorithms implemented in PyTorch

A Complete Collection of Deep RL Famous Algorithms implemented in Gymnasium most Popular environments

Implementations of popular deep RL algorithms. Written in Pytorch

In this repo, I implemented some popular Deep-RL algorithms like DQN and SARSA. Hope you enjoy!

Popular RL algorithms that I'm implementing while learning RL

Custom implementations of some popular reinforcement learning algorithms + exercises from Practical_RL

Implementations of popular RL algorithms applied to steering control in the CARLA simulator!

Compare and benchmark the performance of RL algorithms from two popular libraries, SB3 & RLlib

popular rl algorithms

PyTorch Implementation of popular RL algorithms

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A Python library implementing popular RL, DRL, and MARL Algorithms utilizing Numpy, Pytorch as core libraries.

Benchmarking existing RL algorithms including model-free and model-based approaches on confounded versions of popular environments. Tests generalization and sample efficiency.

An RL based model using PPO algorithm leveraging OpenAI Gym environment to play the popular Super Mario game.

A Reinforcement Learning agent trained to complete at least Level 2 of a Mario game. This project implements popular RL algorithms to teach an AI to navigate obstacles, collect rewards, and finish stages efficiently. Includes training scripts, environment setup, evaluation metrics, and demo runs for Level 1 & 2 gameplay.

(From 2020) ML Course Final Project - Deep Reinforcement Learning has proved useful in solving tasks bounded by an environment/rules. Q-learning, a popular method of RL, has been shown to solve wide varieties of diverse environments. Due to the vigorous study/use of Q-learning, engineers and researchers have developed various add-ons and improvements to this algorithm. Using the once popular game 2048 as our environment, we will analyze these improvements to find the optimal Q-learning policy.

the entire project of modifying drugs in order to generate novel structures. Thisintroduces machine learning algorithms such as Reinforcement Learning algorithms for drug design.Reinforcement learning is how agents take a specific action in an environment in order to increase theoverall rewards. This will be used for drug design in order to find the best possible action to create novelstructure and make a molecule more drug-like.The objective of the project is to create an environment which will allow the agent to make de-cisions/actions based on the state of the environment. In order to achieve this, open source packagessuch as OpenAI Gym, a popular framework for building benchmarking environments for RL agents andRDkit which is a cheminformatics package used in computational chemistry. This environment wouldbe developed into an on going Msc project and integrated in future inverse design and drug discovery research

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Basic popular algorithms of RL

RL implementations of popular algorithms

Implementation of Popular RL algorithms

Implementations of some popular RL algorithms

Implementations of some popular RL algorithms

Implementation of some popular Deep RL algorithms

My implementation of the popular RL algorithms