Introduction

This repository is the tensorflow implementations of the paper DPIQN: Deep Policy Inference Q-Network.

Requirments

Python 3.4+
pygame-soccer
tensorflow 1.2.1
tensorpack

Training

Simply enter the following command to train a DPIQN agent:

 python src/train_dpiqn.py

The following arguments can help you customize your own training arguments:

 --gpu                 comma separated list of GPU(s) to use.
 --load                load model
 --log                 train log dir
 --task                task to perform {play, eval, train}
 --algo                algorithm for computing Q-value {DQN, Double, Dueling}
 --mode                specify ai mode in env (can be list) {offensive, defensive}
 --mt_mode             multi-task setting {coop-only,opponent-only,all}
 --mt                  use 2v2 env
 --skip                act repeat
 --hist_len            hist len
 --batch_size          batch size (default: 32)
 --lr                  init lr value (default: 1e-3)
 --rnn                 use rnn (DRPIQN)
 --lr_sched            lr schedule (default: 600:4e-4,1000:2e-4)
 --eps_sched           eps decay schedule (default: 100:0.1,3200:0.01)
 --reg                 reg

For example, if you run the following command:

python src/train_dpiqn.py --gpu=1 --mt --mt_mode=coop-only --eps_sched='100:0.1,3200:0.01'

Then it will start training a DPIQN model in 2 vs. 2 soccer game, and it will only infer its coolaborator's policy. Besides, the eps parameter for epsilon-greedy will decrease to 0.1 at epoch 100, and down to 0.01 at epochj 3200.

Testing

To test the model, enter the command:

 python src/train_dpiqn.py --load=[path_to_model] --task=eval

The model will be evaluated for 100,000 episodes. In addition, you can use the following command to watch how your agent play:

 python src/train_dpiqn.py --load=[path_to_model] --task=play

Note that you can also use the same optional arguments listed in Training section.

SigmaBM/infer-policy-feature

Introduction

Requirments

Training

Testing