A set of 20 doubly-jointed arms are made to reach target spaces through an Actor-Critic - Deep Deterministic Policy Gradient(DDPG) Algorithm
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.
Random agents for 20 arms | Trained agents for 20 arms |
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Random agent for one arm | Trained agents for one arm |
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The environment is a continuous action space environment. It is considered to be solved if the agents get an average score of +30 (over 100 consecutive episodes, and over all agents)
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Download the environment from one of the links below. You need only select the environment that matches your operating system:
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Version 1: One Agent
- Linux: click here
- Mac OSX: click here
- Windows (32-bit): click here
- Windows (64-bit): click here
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Version 2: Twenty (20) Agents
- Linux: click here
- Mac OSX: click here
- Windows (32-bit): click here
- Windows (64-bit): click here
(For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.
(For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link (version 1) or this link (version 2) to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)
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Place the file in the GitHub repository, in the folder, and unzip (or decompress) the file.
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For Installing this code, you will need to install the dependencies.
Pytorch, Numpy, Matplotlib, unityagents, Jupyter Notebook