In this project, reinforcemnt learning (Deep Deterministic Policy Gradient) is used to control two-jointed arms to reach moving targets. A single agent is trained to control the 20 two-jointed arms.

In the environment, each 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 the agent is to maintain its position at the target location for as many time steps as possible. The environment is considered solved when the average score is above 30 over 100 consecutive episodes. The task is episodic.

The state space and action are continuous. The state space is represented as a state vector. The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is represented as 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 official instructions for installing the unity environment and dependencies can be found here. In addition, the instruction regarding setting up Python Environment can be found here.

Run the jupyter notebook file Continuous_Control_DDPG_v5.ipynb in the repository for training the agent and see the result.