Unsupervised State Representation Learning in Atari

Ankesh Anand, Evan Racah, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm

This repo provides code for the benchmark and techniques introduced in the paper Unsupervised State Representation Learning in Atari

• 📦 Install -- Install relevant dependencies and the project
• 🔧 Usage -- Learn how to use AtariARI and train representations with Spatio-Temporal DeepInfomax (ST-DIM)
• 🕹️ RAM Annotations -- Mapping of RAM indexes to semantic state variables
• 📜 Poster -- Summary of the paper in a poster format
• 🎞️ Slides -- Slides describing the paper

Install

AtariARI Wrapper

You can do a minimal install to get just the AtariARI (Atari Annotated RAM Interface) wrapper by doing:

pip install 'gym[atari]'
pip install git+git://github.com/mila-iqia/atari-representation-learning.git

This just requires

gym[atari]

Full installation (AtariARI Wrapper + Training & Probing Code)

# PyTorch and scikit learn
conda install pytorch torchvision -c pytorch
conda install scikit-learn

Baselines for Atari preprocessing
Tensorflow is a dependency, but you don't need to install the GPU version
conda install tensorflow
pip install git+git://github.com/openai/baselines
pytorch-a2c-ppo-acktr for RL utils
pip install git+git://github.com/ankeshanand/pytorch-a2c-ppo-acktr-gail
Clone and install our package
pip install -r requirements.txt
pip install git+git://github.com/mila-iqia/atari-representation-learning.git

Usage

Atari Annotated RAM Interface (AtariARI):

AtariARI exposes the ground truth labels for different state variables for each observation. We have made AtariARI available as a Gym wrapper, to use it simply wrap an Atari gym env with

AtariARIWrapper

import gym
from atariari.benchmark.wrapper import AtariARIWrapper
env = AtariARIWrapper(gym.make('MsPacmanNoFrameskip-v4'))
obs = env.reset()
obs, reward, done, info = env.step(1)

Now,

info

{'ale.lives': 3,
 'labels': {'enemy_sue_x': 88,
  'enemy_inky_x': 88,
  'enemy_pinky_x': 88,
  'enemy_blinky_x': 88,
  'enemy_sue_y': 80,
  'enemy_inky_y': 80,
  'enemy_pinky_y': 80,
  'enemy_blinky_y': 50,
  'player_x': 88,
  'player_y': 98,
  'fruit_x': 0,
  'fruit_y': 0,
  'ghosts_count': 3,
  'player_direction': 3,
  'dots_eaten_count': 0,
  'player_score': 0,
  'num_lives': 2}}

Note: In our experiments, we use additional preprocessing for Atari environments mainly following Minh et. al, 2014. See atariari/benchmark/envs.py for more info!

If you want the raw RAM annotations (which parts of ram correspond to each state variable), check out atariari/benchmark/ram_annotations.py

Probing

⚠️ Important ⚠️: The RAM labels are meant for full-sized Atari observations (210 * 160). Probing results won't be accurate if you downsample the observations.

We provide an interface for the included probing tasks.

First, get episodes for train, val and, test:

from atariari.benchmark.episodes import get_episodes

tr_episodes, val_episodes,
tr_labels, val_labels,
test_episodes, test_labels = get_episodes(env_name="PitfallNoFrameskip-v4", 
                                     steps=50000, 
                                     collect_mode="random_agent")

Then probe them using ProbeTrainer and your encoder (

my_encoder

from atariari.benchmark.probe import ProbeTrainer

probe_trainer = ProbeTrainer(my_encoder, representation_len=my_encoder.feature_size)
probe_trainer.train(tr_episodes, val_episodes,
                     tr_labels, val_labels,)
final_accuracies, final_f1_scores = probe_trainer.test(test_episodes, test_labels)

To see how we use ProbeTrainer, check out scripts/run_probe.py

Here is an example of

my_encoder

# get your encoder
import torch.nn as nn
import torch
class MyEncoder(nn.Module):
    def __init__(self, input_channels, feature_size):
        super().__init__()
        self.feature_size = feature_size
        self.input_channels = input_channels
        self.final_conv_size = 64 * 9 * 6
        self.cnn = nn.Sequential(
            nn.Conv2d(input_channels, 32, 8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, 4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 128, 4, stride=2),
            nn.ReLU(),
            nn.Conv2d(128, 64, 3, stride=1),
            nn.ReLU()
        )
        self.fc = nn.Linear(self.final_conv_size, self.feature_size)

def forward(self, inputs):
    x = self.cnn(inputs)
    x = x.view(x.size(0), -1)
    return self.fc(x)
my_encoder = MyEncoder(input_channels=1,feature_size=256)
load in weights
my_encoder.load_state_dict(torch.load(open("path/to/my/weights.pt", "rb")))

Spatio-Temporal DeepInfoMax:

src/

ST-DIM

python -m scripts.run_probe --method infonce-stdim --env-name {env_name}

where

env_name

{game}NoFrameskip-v4

PongNoFrameskip-v4

Citation

@article{anand2019unsupervised,
  title={Unsupervised State Representation Learning in Atari},
  author={Anand, Ankesh and Racah, Evan and Ozair, Sherjil and Bengio, Yoshua and C{\^o}t{\'e}, Marc-Alexandre and Hjelm, R Devon},
  journal={arXiv preprint arXiv:1906.08226},
  year={2019}
}