Multi agent PPO implementation in Pytorch for Unity ML Agents environments.
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Updated
Jun 23, 2022 - Python
Multi agent PPO implementation in Pytorch for Unity ML Agents environments.
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM) on Pyramid env, Unity ML
3D Packing with Deep Reinforcement Learning using Unity ML-Agents
Continuous Control with deep reinforcement learning where the agent must reach a moving ball with a double jointed arm
Train an agent using RL to navigate (and collect bananas) in a large, square world
In this repo, I implement deep deterministic policy gradients and multi-agent deep deterministic poilicy gradients to solve the Tennis enironment (Unity ML-Agents)
Keeping the double-jointed arm hand in the green sphere
Deep Reinforcement Learning: Two Competing Tennis Playing Agents
Project 2 of Udacity's Deep Reinforcement Learning Nanodegree Program
Deep Reinforcement Learning: Navigation. Capture yellow bananas while avoiding blue bananas with an agent trained using Deep Q-Networks on a Unity ML-Agent environment.
Training a pair of agents to play tennis
Some of my solutions of the exercises and all my projects of the Udacity Deep Reinforcement Learning Nanodegree https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893
My solutions to the projects within the Udacity Deep Reinforcement Learning Nanodegree.
Project 1 of Udacity's Deep Reinforcement Learning Nanodegree Program
Udacity's Reinforcement Learning Project 1
Small Reinforcement Learning Framework
Project 3 of Udacity's Deep Reinforcement Learning Nanodegree Program
심층강화학습기반 디지털 트윈 환경에서의 자율주행 연구
A pair of reinforcement learning agents that can play tennis 🎾 — Udacity Deep RL Nanodegree Project
Deep Q-Learning Agent mastering the Unity Banana Collector environment! — Udacity Deep RL Nanodegree Project
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