Ablation Analysis on Three Main Components in Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Minimal Implementation of Deep RL Algorithms in PyTorch

Deep Reinforcement Learning with LTL goals.

Projektseminar zu Data Analytics on Blockchain. Team aus Leo Irudayam, Julia Heppler, Sandro Franzoi, Benjamin Bargetz.

Functions for Financial Mathematical calculations - Present Value, Future Value, Equivalent Rate, Financing Systems: SAC, PRICE, SAA.

Modularized Implementation of Deep RL Algorithms in PyTorch

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Mirror Descent Policy Optimization

The implement of the policy gradient RL algorithm with pytorch

Multi-Discrete Soft Actor Critic implementation on Unity's procedurally generated Obstacle Tower Environment.

This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)

Benchmark data (i.e., DeepMind Control Suite and MuJoCo) for RL.

Yet another deep reinforcement learning

Deep Reinforcement Learning- OpenAI Gym

Soft Actor-Critic implementation with SOTA model-free extension (REDQ) and SOTA model-based extension (MBPO).

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

Basic reinforcement learning algorithms. Including:DQN,Double DQN, Dueling DQN, SARSA, REINFORCE, baseline-REINFORCE, Actor-Critic,DDPG,DDPG for discrete action space, A2C, A3C, TD3, SAC, TRPO

PyTorch Deep Reinforcement Learning.

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning