This project is for multi agent RL, which deals in continous action space to take actions such that it maximizes the score of the agents..So that both they play tennis for longer time
Number of Visual Observations (per agent): 0
Vector Observation space type: continuous
Vector Observation space size (per agent): 8
Number of stacked Vector Observation: 3
Vector Action space type: continuous
Vector Action space size (per agent): 2
Vector Action descriptions: In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a 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 task is episodic, and in order to solve the environment, your agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents).
conda install --file python3.ymlTennis.app --> Unity enviornment for Mac devices
checkpoint_actor.pth,checkpoint_critic.pth ----> Saved model
ddpg_agent.py --> agent file for learning
model2.py --> models for actor and critic
run.py --> file for training
mulagent.py --> multi agent file for taking actions and steps
temp.ipynb --> Ipython notebok for training