High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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Updated
Nov 14, 2024 - Python
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
This repository contains the implementation of the K-workers, n-step Advantage Actor-Critic (A2C) algorithm applied to the CartPole environment, as part of a reinforcement learning project for the EPFL Spring Semester 2024 course on Artificial Neural Networks and Reinforcement Learning.
This repository contains the implementation of the K-workers, n-step Advantage Actor-Critic (A2C) algorithm applied to the CartPole environment, as part of a reinforcement learning project for the EPFL Spring Semester 2024 course on Artificial Neural Networks and Reinforcement Learning.
A PyTorch library for building deep reinforcement learning agents.
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Example A2C implementation with ReLAx
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Implementations of deep reinforcement learning algorithms.
This repository contains my assignment solutions for the Deep Learning course (M2177.003100_002) offered by Seoul National University (Fall 2019).
The pytorch implemetation of a2c
PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.
Scalable, event-driven, deep-learning-friendly backtesting library
Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
First Place Reinforcement Learning solution code and a writeup for the AI RoboSoccer Competition.
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