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PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.
Solutions of assignments of Deep Reinforcement Learning course presented by the University of California, Berkeley (CS285) in Pytorch framework
PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.
强化学习算法库，包含了目前主流的强化学习算法(Value based and Policy based)的代码，代码都经过调试并可以运行
A Universal Deep Reinforcement Learning Framework
A trading bitcoin agent was created with deep reinforcement learning implementations.
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.
Chapter notes and exercise solutions for Reinforcement Learning: An Introduction by Sutton and Barto
Implemenation of DDPG with numpy only (without Tensorflow)
PyTorch Implementation of Soft Actor-Critic Algorithm
Solving CartPole-v1 environment in Keras with Actor Critic algorithm an Deep Reinforcement Learning algorithm
Implementation of Reinforcement Algorithms from scratch
Reinforcement Learning - PPO (Proximal Policy Optimization) Implementation to Pong Game
Project Solutions for my Deep Reinforcement Learning Nanodegree at Udacity
A novel method to incorporate existing policy (Rule-based control) with Reinforcement Learning.
Reinforcement learning, Policy Gradient, Actor-Critic, AC, Agent-based Simulation, Simple-world
Reinforcement Learning (RL) 🤖! This repository is your hands-on guide to implementing RL algorithms, from Markov Decision Processes (MDPs) to advanced methods like PPO and DDPG. 🚀 Build smart agents, learn the math behind policies, and experiment with real-world applications! 🔥💡