NEW: extended documentation available at https://rlpyt.readthedocs.io (as of 27 Jan 2020)
View the Change Log
Modular, optimized implementations of common deep RL algorithms in PyTorch, with unified infrastructure supporting all three major families of model-free algorithms: policy gradient, deep-q learning, and q-function policy gradient. Intended to be a high-throughput code-base for small- to medium-scale research (large-scale meaning like OpenAI Dota with 100's GPUs). Key capabilities/features include:
- Run experiments in serial mode (helpful for debugging during development, or maybe sufficient for experiments).
- Run experiments fully parallelized, with options for parallel sampling and/or multi-GPU optimization.
- Multi-GPU optimization uses PyTorch's DistributedDataParallel, which supports gradient reduction concurrent with backprop.
- Use CPU or GPU for training and/or batched action selection during environment sampling.
- Sampling and optimization synchronous or asynchronous (via replay buffer).
- Full support for recurrent agents.
- All agents receive
observation, prev_action, prev_reward
. - Training data always organized with leading indexes as
[Time, Batch]
.
- All agents receive
- Online or offline evaluation of agent diagnostics during training.
- Launching utilities for stacking/queueing sets of experiments in parallel on given local hardware resources (e.g. run 40 experiments on an 8-GPU machine with 1 experiment per GPU at a time).
- Compatible with the OpenAI Gym environment interface.1
- Modularity for easy modification / re-use of existing components.
Policy Gradient A2C, PPO.
Replay Buffers (supporting both DQN + QPG) non-sequence and sequence (for recurrent) replay, n-step returns, uniform or prioritized replay, full-observation or frame-based buffer (e.g. for Atari, stores only unique frames to save memory, reconstructs multi-frame observations).
Deep Q-Learning DQN + variants: Double, Dueling, Categorical (up to Rainbow minus Noisy Nets), Recurrent (R2D2-style). Coming soon: Implicit Quantile Networks?
Q-Function Policy Gradient DDPG, TD3, SAC. Coming soon: Distributional DDPG?
Follow the installation instructions below, and then get started in the examples folder. Example scripts are ordered by increasing complexity.
For newcomers to deep RL, it may be better to get familiar with the algorithms using a different resource, such as the excellent OpenAI Spinning Up: docs, code.
Rlpyt introduces new object classes namedarraytuple
for easier organization of collections of numpy arrays / torch tensors. (see rlpyt/utils/collections.py
). A namedarraytuple
is essentially a namedtuple
which exposes indexed or sliced read/writes into the structure. For example, consider writing into a (possibly nested) dictionary of arrays:
for k, v in src.items():
if isinstance(dest[k], dict):
..recurse..
dest[k][slice_or_indexes] = v
This code is replaced by the following:
dest[slice_or_indexes] = src
Importantly, this syntax looks the same whether dest
and src
are indiviual numpy arrays or arbitrarily-structured collections of arrays (the structures of dest
and src
must match, or src
can be a single value, or None
is an empty placeholder). Rlpyt uses this data structure extensively--different elements of training data are organized with the same leading dimensions, making it easy to interact with desired time- or batch-dimensions.
This is also intended to support environments with multi-modal observations or actions. For example, rather than flattening joint-angle and camera-image observations into one observation vector, the environment can store them as-is into a namedarraytuple
for the observation, and in the forward method of the model, observation.joint
and observation.image
can be fed into the desired layers. Intermediate infrastructure code doesn’t change.
Overall the code is stable, but might still develop, changes may occur. Open to suggestions/contributions for other established algorithms to add or other developments to support more use cases--please see our simple contribution guidelines.
This package does not include its own visualization, as the logged data is compatible with previous editions (see below). For more features, use https://github.com/vitchyr/viskit.
Install directly with:
pip install git+https://github.com/sagar-pa/rlpyt.git
OR
-
Clone this repository to the local machine.
-
Install the anaconda environment appropriate for the machine.
pip install -e .[extra]
For more discussion, please see the white paper on Arxiv. If you use this repository in your work or otherwise wish to cite it, please make reference to the white paper.
The class types perform the following roles:
- Runner - Connects the
sampler
,agent
, andalgorithm
; manages the training loop and logging of diagnostics.- Sampler - Manages
agent
/environment
interaction to collect training data, can initialize parallel workers.- Collector - Steps
environments
(and maybe operatesagent
) and records samples, attached tosampler
.- Environment - The task to be learned.
- Observation Space/Action Space - Interface specifications from
environment
toagent
.
- Observation Space/Action Space - Interface specifications from
- TrajectoryInfo - Diagnostics logged on a per-trajectory basis.
- Environment - The task to be learned.
- Collector - Steps
- Agent - Chooses control action to the
environment
insampler
; trained by thealgorithm
. Interface tomodel
.- Model - Torch neural network module, attached to the
agent
. - Distribution - Samples actions for stochastic
agents
and defines related formulas for use in loss function, attached to theagent
.
- Model - Torch neural network module, attached to the
- Algorithm - Uses gathered samples to train the
agent
(e.g. defines a loss function and performs gradient descent).- Optimizer - Training update rule (e.g. Adam), attached to the
algorithm
. - OptimizationInfo - Diagnostics logged on a per-training batch basis.
- Optimizer - Training update rule (e.g. Adam), attached to the
- Sampler - Manages
This code is a revision and extension of accel_rl, which explored scaling RL in the Atari domain using Theano. Scaling results were recorded here: A. Stooke & P. Abbeel, "Accelerated Methods for Deep Reinforcement Learning". For an insightful study of batch-size scaling across deep learning including RL, see S. McCandlish, et. al "An Empirical Model of Large-Batch Training".
Accel_rl was inspired by rllab (the logger
here is nearly a direct copy). Rlpyt follows the rllab interfaces: agents output action, agent_info
, environments output observation, reward, done, env_info
. In general in rlpyt, agent inputs/outputs are torch tensors, and environment inputs/ouputs are numpy arrays, with conversions handled automatically.
- Regarding OpenAI Gym compatibility, rlpyt uses a
namedtuple
forenv_info
rather than adict
. This makes for easier data recording but does require the same fields to be output at every environment step. An environment wrapper is provided. Wrappers are also provided for Gym spaces to convert to rlpyt spaces (notablyDict
tocomposite
).
Thanks for support / mentoring from Pieter Abbeel, the Fannie & John Hertz Foundation, NVIDIA, Max Jaderberg, OpenAI, and the BAIR community. And thanks in advance to any contributors!
Happy reinforcement learning!