deep-deterministic-policy-gradient

#计算机科学#Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

#计算机科学#JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

A PyTorch library for building deep reinforcement learning agents.

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

#计算机科学#Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

#计算机科学#lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.

#计算机科学#Implementation of algorithms for continuous control (DDPG and NAF).

#计算机科学#End to end motion planner using Deep Deterministic Policy Gradient (DDPG) in gazebo

PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.

Mapless Collision Avoidance of Turtlebot3 Mobile Robot Using DDPG and Prioritized Experience Replay

Repository for Planar Bipedal walking robot in Gazebo environment using Deep Deterministic Policy Gradient(DDPG) using TensorFlow.

A Pytorch DQN and DDPG implementation for a smart home energy management system under varying electricity price.

scalable multi agents reinforcement learning

Reinforcement learning in JavaScript & Node.js

Pytorch implementation of the Deep Deterministic Policy Gradients for Continuous Control

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.

Option hedging strategies are investigated using two reinforcement learning algorithms: deep Q network and deep deterministic policy gradient.

#计算机科学#Implementation of Deep Deterministic Policy Gradients (DDPG) to teach a Quadcopter How to Fly!

Designing a control system to exploit model-free deep reinforcement learning algorithms to solve a real-world autonomous driving task of a small robot.