udacity reinforcement learning project 3: Collaboration and Competition.

For this project, student have to train an 2 agents to playing tennis.

A reward of +0.1 receives by an agent if ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.

The state space consists of 8 variables corresponding to the position and velocity of the ball and racket.

Each action is a vector with 2 numbers, corresponding to movement toward (or away from) the net, and jumping. Every entry in the action vector must be a number between -1 and 1.

The task is episodic, and in order to solve the environment, agent must get an average score of +0.5 over 100 consecutive episodes.

The following python3 libraries are required:

numpy == 1.16.2

pytorch == 0.4.0 - (GPU enabled)

unity ML-agent - available at github

This notebook contains full pipeline of training networks:

• Initialization a unity environment;
• Initialization Replay buffer and Agents determined in ddpg_collab_agent.py
• Initialization Actor and Critic neural networks determined in model.py
• Training and saving neural networks models at models folder.