Tennis Game play using Multi Agent DDPG - Deep Reinforcement Learning

This Repository Contains PyTorch Implementation of Multi Agent DDPG Algorithm to train agents to play tennis.

The third project of Udacity Deep Reinforcement Learning Nanodegree

Train the model using Multi Agent DDPG of deep reinforcement learning with Tennis Environment in the Unity Agents

Usage of Unity ML-Agents train two agents to play tennis

Experimenting with Policy Gradient Reinforcement Learning Algorithms and Evolutionary Strategies in a warehouse congestion-management domain.

Trained a pair of agents to play tennis. Reinforcement Learning methods were used along with various Deep Learning algorithms.

POMDP and MADDPG

Two deep-reinforcement learning agents that play tennis.

Multi agent reinforcement learning: PyTorch implementations of several algorithms for Multi Agent domains

The pytorch implementation of maddpg

Implement MADDPG to solve Collaboration and Competition problem in Unity

My solutions to the projects within the Udacity Deep Reinforcement Learning Nanodegree.

Project 3 of Udacity's Deep Reinforcement Learning nanodegree program

Learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles.

Using MADDPG algorithm to solve the unity tennis environment.

Implementation of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for the Tennis environment in the context of "Collaboration and Competition", the third Udacity Deep Reinforcement Learning Nanodegree project.

Udacity DRL nanodegree project 3 submission