DeepMind Atari Deep Q Learner for 2 players

This repository hosts the code to reproduce the experiments in the article "Multiagent Cooperation and Competition with Deep Reinforcement Learning". It is based on DeepMind's original code, that was modified to support two players. NB! Currently only Pong game in two-player mode is supported, support for other games and one-player mode is untested.

Gameplay videos can be found here: https://www.youtube.com/playlist?list=PLfLv_F3r0TwyaZPe50OOUx8tRf0HwdR_u

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 Atari games, the following components must be installed:

• LuaJIT and Torch 7.0
• nngraph
• Xitari (fork of the Arcade Learning Environment (Bellemare et al., 2013))
• AleWrap (a lua interface to Xitari)

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, libqt4-dev.

In addition following Lua components are installed to 'torch' subdirectory: luajit-rocks, cwrap, paths, torch, nn, cutorch, cunn, luafilesystem, penlight, sys, xlua, image, env, qtlua, qttorch, nngraph, lua-gd.

Training

To run training for a game:

./run_gpu2 <game name>

Following games are supported:

• Pong2Player - cooperative game (\rho = -1)
• Pong2Player075 - transition (\rho = -0.75)
• Pong2Player05 - transition (\rho = -0.5)
• Pong2Player025 - transition (\rho = -0.25)
• Pong2Player0 - transition (\rho = 0)
• Pong2Player025p - transition (\rho = 0.25)
• Pong2Player05p - transition (\rho = 0.5)
• Pong2Player075p - transition (\rho = 0.75)
• Pong2PlayerVS - competitive game (\rho = 1)

During training the snapshots of networks of both agents are written to dqn/ folder. These are named DQN3_0_1_<game name>_FULL_Y_A_<epoch>.t7 and DQN3_0_1_<game name>_FULL_Y_B_<epoch>.t7. One epoch is defined as 250,000 steps and they are numbered starting from 0. NB! One epoch snapshot takes about 1GB, therefore for 50 epochs reserve 50GB free space.

Testing

To run testing for one episode:

./test_gpu2 <game name> <epoch>

To run testing with different seeds (by default 10):

./test_gpu2_seeds <game name> <epoch>

To run testing with different seeds (by default 10), for all epochs (default 49):

./test_gpu2_versions <game name>

To run all experiments at once:

./test_schemes

All these scripts write file dqn/<game name>.csv, that contains following game statistics:

• Epoch - epoch number,
• Seed - seed used for this run,
• WallBounces - total number of wall-bounces in this run,
• SideBounce - total number of paddle-bounces in this run,
• Points - total number of points (lost balls) in this run,
• ServingTime - total serving time in this run,
• RewardA - total reward of player A,
• RewardB - total reward of player B.

NB! All scripts append to this file, so after several runs you might want to delete irrelevant lines.

Extracting training statistics

To plot training history:

./plot_2results <game name> [<epoch>]

Following plots are shown for both agents:

• average reward per game during testing,
• total count of non-zero rewards during testing,
• number of games played during testing,
• average Q-value of validation set.

To extract training statistics to file:

./extract_data <game name> <epoch>

This produces files dqn/<game name>_history_A.csv and dqn/<game name>_history_B.csv. These files contain following columns:

• Epoch - testing phase number, divide by 2 to get true epoch,
• Average reward - average reward per game during testing,
• Reward count - total count of non-zero rewards during testing,
• Episode count - number of games played during testing,
• MeanQ - average W-value of validation set,
• TD Error - temporal difference error,
• Seconds - seconds since start.

Plotting game statistics

Plotting scripts are in folder plots. All .csv files from dqn/ folder should be moved there for plotting.

• scatter.py - plots for figure 7, uses <game name>.csv files,
• plot.py - plots for figures 3 and 4, uses Pong2Player.csv and Pong2PlayerVS.csv files,
• plot_history.py - plots for figure 8, uses <game name>_history_A.csv and <game name>_history_B.csv files.

NB! Be sure to clean up <game name>.csv files as explained above.