An Optimistic Perspective on Offline Reinforcement Learning (ICML, 2020)

This project provides the open source implementation using the Dopamine framework for running experiments mentioned in An Optimistic Perspective on Offline Reinforcement Learning. In this work, we use the logged experiences of a DQN agent for training off-policy agents (shown below) in an offline setting (i.e., batch RL) without any new interaction with the environment during training. Refer to offline-rl.github.io for the project page.

How to train offline agents on 50M dataset without RAM errors?

Please refer to https://github.com/google-research/batch_rl/issues/10.

DQN Replay Dataset (Logged DQN data)

The DQN Replay Dataset was collected as follows: We first train a DQN agent, on all 60 Atari 2600 games with sticky actions enabled for 200 million frames (standard protocol) and save all of the experience tuples of (observation, action, reward, next observation) (approximately 50 million) encountered during training.

This logged DQN data can be found in the public GCP bucket gs://atari-replay-datasets which can be downloaded using gsutil. To install gsutil, follow the instructions here.

After installing gsutil, run the command to copy the entire dataset:

gsutil -m cp -R gs://atari-replay-datasets/dqn

To run the dataset only for a specific Atari 2600 game (e.g., replace GAME_NAME by Pong to download the logged DQN replay datasets for the game of Pong), run the command:

gsutil -m cp -R gs://atari-replay-datasets/dqn/[GAME_NAME]

This data can be generated by running the online agents using batch_rl/baselines/train.py for 200 million frames (standard protocol). Note that the dataset consists of approximately 50 million experience tuples due to frame skipping (i.e., repeating a selected action for k consecutive frames) of 4. The stickiness parameter is set to 0.25, i.e., there is 25% chance at every time step that the environment will execute the agent's previous action again, instead of the agent's new action.

Publications & submissions using DQN Replay Dataset (please open a pull request for missing entries):

• Revisiting Fundamentals of Experience Replay (ICML 2020)
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (NeurIPS 2020)
• Conservative Q-Learning for Offline Reinforcement Learning (NeurIPS 2020)
• Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning (ICLR 2021)
• Acme: A new framework for distributed reinforcement learning (arXiv 2020)
• Addressing Extrapolation Error in Deep Offline Reinforcement Learning (Submission, ICLR 2021)

Asymptotic Performance of offline agents on Atari-replay dataset

Installation

Install the dependencies below, based on your operating system, and then install Dopamine, e.g.

pip install git+https://github.com/google/dopamine.git

Finally, download the source code for batch RL, e.g.

git clone https://github.com/google-research/batch_rl.git

Ubuntu

If you don't have access to a GPU, then replace tensorflow-gpu with tensorflow in the line below (see Tensorflow instructions for details).

sudo apt-get update && sudo apt-get install cmake zlib1g-dev
pip install absl-py atari-py gin-config gym opencv-python tensorflow-gpu

Mac OS X

brew install cmake zlib
pip install absl-py atari-py gin-config gym opencv-python tensorflow

Running Tests

Assuming that you have cloned the batch_rl repository, follow the instructions below to run unit tests.

Basic test

You can test whether basic code is working by running the following:

cd batch_rl
python -um batch_rl.tests.atari_init_test

Test for training an agent with fixed replay buffer

To test an agent using a fixed replay buffer, first generate the data for the Atari 2600 game of Pong to $DATA_DIR.

export DATA_DIR="Insert directory name here"
mkdir -p $DATA_DIR/Pong
gsutil -m cp -R gs://atari-replay-datasets/dqn/Pong/1 $DATA_DIR/Pong

Assuming the replay data is present in $DATA_DIR/Pong/1/replay_logs, run the FixedReplayDQNAgent on Pong using the logged DQN data:

cd batch_rl
python -um batch_rl.tests.fixed_replay_runner_test \
  --replay_dir=$DATA_DIR/Pong/1

Training batch agents on DQN data

The entry point to the standard Atari 2600 experiment is batch_rl/fixed_replay/train.py. Run the batch DQN agent using the following command:

python -um batch_rl.fixed_replay.train \
  --base_dir=/tmp/batch_rl \
  --replay_dir=$DATA_DIR/Pong/1 \
  --gin_files='batch_rl/fixed_replay/configs/dqn.gin'

By default, this will kick off an experiment lasting 200 training iterations (equivalent to experiencing 200 million frames for an online agent).

To get finer-grained information about the process, you can adjust the experiment parameters in batch_rl/fixed_replay/configs/dqn.gin, in particular by increasing the FixedReplayRunner.num_iterations to see the asymptotic performance of the batch agents. For example, run the batch REM agent for 800 training iterations on the game of Pong using the following command:

python -um batch_rl.fixed_replay.train \
  --base_dir=/tmp/batch_rl \
  --replay_dir=$DATA_DIR/Pong/1 \
  --agent_name=multi_head_dqn \
  --gin_files='batch_rl/fixed_replay/configs/rem.gin' \
  --gin_bindings='FixedReplayRunner.num_iterations=1000' \
  --gin_bindings='atari_lib.create_atari_environment.game_name = "Pong"'

More generally, since this code is based on Dopamine, it can be easily configured using the gin configuration framework.

Dependencies

The code was tested under Ubuntu 16 and uses these packages:

• tensorflow-gpu>=1.13
• absl-py
• atari-py
• gin-config
• opencv-python
• gym
• numpy

Citing

If you find this open source release useful, please reference in your paper:

Agarwal, R., Schuurmans, D. & Norouzi, M.. (2020). An Optimistic Perspective on Offline Reinforcement Learning International Conference on Machine Learning (ICML).

@inproceedings{agarwal2020optimistic,
  title={An Optimistic Perspective on Offline Reinforcement Learning},
  author={Agarwal, Rishabh and Schuurmans, Dale and Norouzi, Mohammad},
  journal={International Conference on Machine Learning},
  year={2020}
}

Note: A previous version of this work was titled "Striving for Simplicity in Off Policy Deep Reinforcement Learning" and was presented as a contributed talk at NeurIPS 2019 DRL Workshop.

Disclaimer: This is not an official Google product.