A pytorch tutorial for DRL(Deep Reinforcement Learning)

Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.

A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.

The implement of all kinds of dqn reinforcement learning with Pytorch

PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER, Noisy layer, N-step bootstrapping, Dueling architecture and parallel env support.

PyTorch implementation of D4PG with the SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.

General-Purpose Reinforcement Learning

Collection of reinforcement learning algorithms implementations with TensorFlow2

A TF2.0 implementation of RL baselines.

Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.

This is a reconstruction of previous repository(rl-algorithms).

Durham University, Dissertation: 1st - 92. Additional Materials and Codebase for the paper: Combining Recent Advances in Reinforcement Learning for Super Mario Bros. - Recurrent Replay Deeper Denser Distributed DQN+ (R2D4+).

Rainbow, IQN on atari games

Distributed PyTorch implementation of D4PG with ray. Using a SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.

IQN datatype in Python

iSCSI setup for server-storage