There are 1 repository under a2c topic.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
Massively Parallel Deep Reinforcement Learning. 🔥
Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch
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).
Modularized Implementation of Deep RL Algorithms in PyTorch
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code
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)
A PyTorch library for building deep reinforcement learning agents.
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)
PyTorch C++ Reinforcement Learning
PyTorch implementation of deep reinforcement learning algorithms
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
Reinforcement learning tutorials
A library for ready-made reinforcement learning agents and reusable components for neat prototyping
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
A pytorch tutorial for DRL(Deep Reinforcement Learning)
Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"
Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO
A Clearer and Simpler Synchronous Advantage Actor Critic (A2C) Implementation in TensorFlow
PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.
Implementations of Deep Reinforcement Learning Algorithms and Bench-marking with PyTorch
StarCraft II / PySC2 Deep Reinforcement Learning Agents (A2C)
A drone control system based on deep reinforcement learning with Tensorflow and ROS
Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
Adversarial attacks on Deep Reinforcement Learning (RL)