Implementation of Proximal Policy Optimization algorithm on a custom Unity environment.
-
Updated
Feb 3, 2022 - ASP.NET
Implementation of Proximal Policy Optimization algorithm on a custom Unity environment.
Implementation of project 3 for Udacity's Deep Reinforcement Learning Nanodegree
An implementation of MADDPG multi-agent to solve a Unity environment like Tennis and Soccer.
An unofficial library for interacting with Discord Webhook aimed at use in the Unity environment
Deep Reinforcement Learning Projects
Implementation of project 2 for Udacity's Deep Reinforcement Learning Nanodegree
We train an agent to manuver in a 3-D environment avoiding blue bananas and picking yellow ones as fast as possible.
This is the 2nd project in Udacity DRLND, which is practice for training an agent that controls a robotic arm in Unity's Reacher environment using the Deep Deterministic Policy Gradients (DDPG) algorithm.
Multiagent RL
Implementation of project 1 for Udacity's Deep Reinforcement Learning Nanodegree
Create and train a double-jointed arm agent that is able to maintain its hand in contact with a moving target
Collaboration and Competition (using multi agent reinforcement learning). Train a pair of agents to play tennis.
Deep reinforcement Learning Nanodegree - Navigation Project
Training an agent to perform continuous task
Solution of a first project of the deep reinforcement learning nanodegree at Udacity.
Solving Reacher environment using deep reinforcement learning
Train double-jointed arms to reach target locations using Proximal Policy Optimization (PPO) in Pytorch
An implementation of DDPG agent to solve a Unity environment like Reacher and Crawler.
Add a description, image, and links to the unity-environment topic page so that developers can more easily learn about it.
To associate your repository with the unity-environment topic, visit your repo's landing page and select "manage topics."