Deep reinforcement Learning Nanodegree - Navigation Project
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Updated
Jan 8, 2019 - Python
Deep reinforcement Learning Nanodegree - Navigation Project
Solving Reacher environment using deep reinforcement learning
Solution of a first project of the deep reinforcement learning nanodegree at Udacity.
Training an agent to perform continuous task
An implementation of Deep Q-Learning Network for solving a Unity environment that can navigate and collect bananas in a large, square world.
This is a game developed in unity to understand the basics of unity environment.
Using Deep Deterministic Policy Gradient(DDPG) to train a robotic arm to reach target locations, simulated on a unity environment
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.
Reinforcement Learning using Unity ML - Agents
Training a pair of competing RL agents to play Tennis using MADDPG algorithm
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.
Implementation of project 1 for Udacity's Deep Reinforcement Learning Nanodegree
Multiagent RL
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.
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
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