DQN_for_SimpleMaze

Deep Q-network for simple maze. The agent can find a best route.It's very interesting.Its require torch7.

Here is the original README:

DQN 3.0

This project contains the source code of DQN 3.0, a Lua-based deep reinforcement learning architecture, necessary to reproduce the experiments described in the paper "Human-level control through deep reinforcement learning", Nature 518, 529–533 (26 February 2015) doi:10.1038/nature14236.

To replicate the experiment results, a number of dependencies need to be installed, namely:

• LuaJIT and Torch 7.0
• nngraph
• Xitari (fork of the Arcade Learning Environment (Bellemare et al., 2013))
• AleWrap (a lua interface to Xitari) An install script for these dependencies is provided.

Two run scripts are provided: run_cpu and run_gpu. As the names imply, the former trains the DQN network using regular CPUs, while the latter uses GPUs (CUDA), which typically results in a significant speed-up.

Installation instructions

The installation requires Linux with apt-get.

Note: In order to run the GPU version of DQN, you should additionally have the NVIDIA® CUDA® (version 5.5 or later) toolkit installed prior to the Torch installation below. This can be downloaded from https://developer.nvidia.com/cuda-toolkit and installation instructions can be found in http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux

To train DQN on Simple maze, the following components must be installed:

• LuaJIT and Torch 7.0
• nngraph
• Xitari
• AleWrap

To install all of the above in a subdirectory called 'torch', it should be enough to run

./install_dependencies.sh

from the base directory of the package.

Note: The above install script will install the following packages via apt-get: build-essential, gcc, g++, cmake, curl, libreadline-dev, git-core, libjpeg-dev, libpng-dev, ncurses-dev, imagemagick, unzip

Training DQN on Atari games

Prior to running DQN on a game.It should then be sufficient to run the script

./run_cpu <maze>

Or, if GPU support is enabled,

./run_gpu <maze>

Note: On a system with more than one GPU, DQN training can be launched on a specified GPU by setting the environment variable GPU_ID, e.g. by

GPU_ID=2 ./run_gpu <maze>

If GPU_ID is not specified, the first available GPU (ID 0) will be used by default.

Options

Options to DQN are set within run_cpu (respectively, run_gpu). You may, for example, want to change the frequency at which information is output to stdout by setting 'prog_freq' to a different value.

• uses qtlua and image.display() to show game screen while training,
• qlua plot_results.lua script to plot history recorded in model file,
• test_gpu <maze> and test_cpu <maze> scripts to play one session and record screens in animated GIF. These also record actions, Q-values and rewards in CSV file.