Overview

RLtime is a reinforcement learning library, currently supporting PyTorch, with focus on state-of-the-art q-learning algorithms and features, and interacting with real-time environments which require low-latency acting and sample-efficient training.

Supported Policies and Features

Following policies are currently supported:

• DQN (Deep Q Networks)
• IQN (Implicit Quantile Networks)
• Distributional DQN / C51 (Unverified)
• Actor Critic (Trainable via A2C or PPO)

All q-learning algorithms commonly support any combination of most state-of-the-art features, in particular:

• All rainbow features (except noisy nets) are supported:
  • Double Q-Learning
  • Dueling Networks
  • Multi-Step Targets
  • Prioritized Replay
• R2D2 features:
  • Value Function Rescaling (Instead of reward clipping)
  • Weighted multi-step/multi-env prioritized replay (With configurable sequence overlap)
  • Stored recurrent state
  • Recurrent state burn-in

All algorithms support recurrent models with dynamic model layout configuration using json files, with extended syntax for referencing/embedding other json files and python types.

Atari Results

By extending IQN with an LSTM model as well as some rainbow and R2D2 features we can achieve state-of-the-art (in sample efficiency, 200M frames) results on the Atari benchmark:

^{* For 55 out of 57 Atari-57 games, single run per game}

A write-up with additional details and full results can be found here.

Installation

git clone https://github.com/opherlieber/rltime.git
cd rltime
pip install -e .

# For tensorboard logging (Requires PyTorch 1.1+ and tensorboard should be at least version 1.14)
pip install tensorboard Pillow

For distributed acting using Ray see the ray docs for ray installation and cluster setup instructions, as well as additional details here.

Usage

# Training a config
python -um rltime.train cartpole_ppo.json
# Training a config with a specific ENV
python -um rltime.train atari_iqn_lstm.json --env BreakoutNoFrameskip-v4

# Evaluate a training result on 32 parallel ENVs for 100 total episodes
# (Result is logged to 'eval.json' in <result_dir>)
python -um rltime.eval <result_dir> --num-envs 32 --episodes 100

# Evaluate a training result and render to the screen in real-time, 4 ENVs tiled
python -um rltime.eval <result_dir> --num-envs 4 --episodes 10 --render

Acting/Training

The library implements a full separation between acting and training. Any acting configuration can be run with any policy/training configuration.

The following acting modes are supported:

• Vectorized multi-env local/synchronized acting using the training policy directly
• High-throughput async/distributed acting (Using Ray, still experimental and not fully verified)

History Buffers

The library has a full separation between acting/training and the history buffer used to accumulate acting data and generate training batches. In particular we can run any training algorithm using any history buffer:

• Online History (E.g. for actor-critic algos, or online nstep q-learning using any of the q-learning algos)
• Replay and Prioritized Replay (Including support for weighted multi-env/multi-step prioritized sequence replay)

Backend

RLtime currently only supports pytorch as a backend (1.0+, 1.1+ recommended), though there is some preparation to possibly add support for TF2.0 later and commonize some of the code.

In general, any code outside of a 'torch' subdirectory should currently be backend independent.

Citing the Project

Please cite this repository if using RLtime in your research or work:

@misc{rltime,
  author = {Lieber, Opher},
  title = {RLtime: A reinforcement learning library for state-of-the-art q-learning},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/opherlieber/rltime}},
}