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qfettes / DeepRL-Tutorials

Star 1.1k

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

reinforcement-learning gae deep-reinforcement-learning rainbow python3 pytorch deep-q-network actor-critic double-dqn quantile-regression dueling-dqn categorical-dqn ppo advantage-actor-critic a2c deep-recurrent-q-network prioritized-experience-replay multi-step-learning noisy-networks deeprl-tutorials
  • Updated May 19, 2021
  • Jupyter Notebook

BY571 / Soft-Actor-Critic-and-Extensions

Star 272

PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.

reinforcement-learning parallel-computing pytorch multi-environment reinforcement-learning-algorithms continuous sac prioritized-experience-replay actor-critic-algorithm soft-actor-critic emphasizing-recent-experience munchausen-reinforcement-learning munchausen d2rl
  • Updated Feb 24, 2021
  • Python

BY571 / DQN-Atari-Agents

Star 120

DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

multiprocessing parallel-computing deep-reinforcement-learning rainbow multi-environment openai reinforcement-learning-algorithms atari c51 reinforcement-learning-agent drqn prioritized-experience-replay dqn-pytorch multi-step-dqn ddqn-pyotrch dueling-dqn-pytorch noisy-dqn n-step-dqn
  • Updated Dec 18, 2020
  • Jupyter Notebook

BY571 / IQN-and-Extensions

Star 81

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.

reinforcement-learning parallel-computing rainbow pytorch multi-environment dqn reinforcement-learning-algorithms parallel-processing iqn prioritized-experience-replay pytorch-implementation implicit-quantile-networks n-step-bootstrapping distributional-r noisy-layer munchausen
  • Updated Mar 4, 2023
  • Jupyter Notebook

Rahul-Choudhary-3614 / Deep-Reinforcement-Learning-Notebooks

Star 42

This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.

deep-reinforcement-learning rainbow doom dqn sarsa a3c ddqn ddpg-algorithm ppo a2c prioritized-experience-replay cartpole-v0 noisy-networks soft-actor-critic half-cheetah kungfumaster-v0 ant-v2 reinforce-algorithm
  • Updated Apr 24, 2021
  • Jupyter Notebook

BY571 / FQF-and-Extensions

Star 29

PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.

reinforcement-learning multiprocessing pytorch multi-environment reinforcement-learning-algorithms parallel-processing dueling-network-architecture prioritized-experience-replay quantile-functions dqn-pytorch distributional-rl noisy-layer fqf nstep-bootstrapping
  • Updated Oct 10, 2020
  • Jupyter Notebook

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