For this project, we work with the Reacher environment.
In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.
The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.
The task is episodic, and in order to solve the environment, the agent must get an average score of +30 over 100 consecutive episodes.
The environment can be found here:
Linux: click here
Mac OSX: click here
Windows (32-bit): click here
Windows (64-bit): click here
The file Continuous_Control.ipynb is a python notebook that contains the solution. Running it will create the environment. The Deep Deterministic Policy Gradient (DDPG) method and experience replay are used. The neural network archetecture is implemented in model.py and the agent in ddpg_agent.py.