BY571/CEN-Network

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Created about 3 years ago
Updated about 3 years ago

BY571/CEN-Network

BY571

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Soft-Actor-Critic-and-Extensions

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

DQN-Atari-Agents

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

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CQL

PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.

Upside-Down-Reinforcement-Learning

Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.

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Deep-Reinforcement-Learning-Algorithm-Collection

Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.

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IQN-and-Extensions

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.

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Munchausen-RL

PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN

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SAC_discrete

PyTorch implementation of the discrete Soft-Actor-Critic algorithm.

Implicit-Q-Learning

PyTorch implementation of the implicit Q-learning algorithm (IQL)

FQF-and-Extensions

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.

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QR-DQN

PyTorch implementation of QR-DQN: Distributional Reinforcement Learning with Quantile Regression

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Normalized-Advantage-Function-NAF-

PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method

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Randomized-Ensembled-Double-Q-learning-REDQ-

Pytorch implementation of Randomized Ensembled Double Q-learning (REDQ)

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D4PG

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.

GANs

ClusterGAN PyTorch implementation

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Medium_Code_Examples

Implementation of fundamental concepts and algorithms for reinforcement learning

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OFENet

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Genetic-Algorithms-Neural-Network-Optimization

Genetic Algorithm for Neural Network Architecture and Hyperparameter Optimization and Neural Network Weight Optimization with Genetic Algorithm

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GARNE-Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration

GARNE: Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration

MBPO

Hindsight-Experience-Replay

Jupyter Notebook

D4PG-ray

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.

pytorch-vmpo

PyTorch implementation of V-MPO

PETS-MPC

RA-PPO

PyTorch implementation of Risk-Averse Policy Learning

Udacity-DRL-Nanodegree-P3-Multiagent-RL-

Multi-Agent-RL Competition on Unitys Tennis Environment

TD3-and-Extensions

PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3) - including additional Extension to improve the algorithm's performance.

DRQN

Jupyter Notebook