helmogey / drlnd-p1-banana

Project 1 on DQN for Udacity - Deep Reinforcement Learning Nanodegree

Banana Navigation Project for DRLND Udacity Nanodegree

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This is the first project in the Udacity Deep Reinforcement Learning Nanodegree. It requires students to develop and train a Deep Q-Network (DQN) model to collect yellow bananas in a simulator.

This project is the first to be solved in the Udacity Deep Reinforcement Learning Nanodegree. It is based in an implementation of a Deep Q-Network (DQN) model. The environment is modeled in Unity, and the task is to train an agent to collect yellow bananas (getting a reward of +1) and avoid non yellow ones (getting -1 reward) c:

Project Details

The simulation contains a single agent that navigates a large environment. At each time step, it has four actions at its disposal:

• 0 - walk forward
• 1 - walk backward
• 2 - turn left
• 3 - turn right

The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana.

The environment is considered solved when the average reward (over the last 100 episodes) is at least +13.

Getting Started

See the instrucions here to set up your environment instructions here

It also requires Unity ML-Agents, NumPy and PyTorch

Get the environment matching your OS :

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

Use full path file reference for such environment. Note that Banana.app is already included in this repo, so it can be imported with:

env = UnityEnvironment(file_name="Banana.app")

Instructions

Then run the navigation_banana.ipynb notebook using the drlnd kernel to train the DQN agent.

After trainig the model, parameters will be dumpt to checkpoint.pth and will be used by the trained agent.