This project, uses 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 the 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.

Two separate versions of the Unity environment are provided:

• The first version contains a single agent.
• The second version contains 20 identical agents, each with its own copy of the environment.

The second version is useful for algorithms like PPO, A3C, and D4PG that use multiple (non-interacting, parallel) copies of the same agent to distribute the task of gathering experience.

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 barrier for solving the second version of the environment is slightly different, to take into account the presence of many agents. In particular, agents must get an average score of +30 (over 100 consecutive episodes, and over all agents). Specifically,

• After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 20 (potentially different) scores. The average of these 20 scores is then taken.
• This yields an average score for each episode (where the average is over all 20 agents).

The environment is considered solved, when the average (over 100 episodes) of those average scores is at least +30.

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

   • Version 1: One (1) Agent

     • Linux: click here
     • Mac OSX: click here
     • Windows (32-bit): click here
     • Windows (64-bit): click here

   • Version 2: Twenty (20) Agents

     • Linux: click here
     • Mac OSX: click here
     • Windows (32-bit): click here
     • Windows (64-bit): click here

2. Place the file in the GitHub repository, in the DRLND_P2_Continous Control/ folder, and unzip (or decompress) the file.

Dependencies, resources, links and further information can be found in the main repository Udacity DRLND. Requirements include setting up PyTorch, the ML-Agents toolkit, and a few more Python packages.

After managing all dependencies, please follow the instructions in Continuous_Control.ipynb.

A more difficult Crawler environment.

In this continuous control environment, the goal is to teach a creature with four legs to walk forward without falling.

You can read more about this environment in the ML-Agents GitHub here. To solve this harder task, you'll need to download a new Unity environment.

You need only select the environment that matches your operating system:

• Linux: click here
• Mac OSX: click here
• Windows (32-bit): click here
• Windows (64-bit): click here

Then, place the file in the DRLND_P2_Continous Control/ folder in the DRLND GitHub repository, and unzip (or decompress) the file. Next, open Crawler.ipynb and follow the instructions to learn how to use the Python API to control the agent.