d3rlpy 0.51

Data-driven Deep Reinforcement Learning Library as an Out-of-the-box Tool

d3rlpy: A data-driven deep reinforcement learning library as an out-of-the-box tool

d3rlpy is a data-driven deep reinforcement learning library as an out-of-the-box tool.

from d3rlpy.dataset import MDPDataset
from d3rlpy.algos import CQL

# MDPDataset takes arrays of state transitions
dataset = MDPDataset(observations, actions, rewards, terminals)

# train data-driven deep RL
cql = CQL()
cql.fit(dataset.episodes)

# ready to control
actions = cql.predict(x)

Documentation: https://d3rlpy.readthedocs.io

key features

:zap: Designed for Data-Driven Deep Reinforcement Learning

d3rlpy is designed for data-driven deep reinforcement learning algorithms where the algorithm finds the good policy within the given dataset, which is suitable to tasks where online interaction is not feasible. d3rlpy also supports the conventional online training paradigm to fit in with any cases.

:beginner: Easy-To-Use API

d3rlpy provides state-of-the-art algorithms through scikit-learn style APIs without compromising flexibility that provides detailed configurations for professional users. Moreoever, d3rlpy is not just designed like scikit-learn, but also fully compatible with scikit-learn utilites.

:rocket: Beyond State-Of-The-Art

d3rlpy provides further tweeks to improve performance of state-of-the-art algorithms potentially beyond their original papers. Therefore, d3rlpy enables every user to achieve professional-level performance just in a few lines of codes.

installation

d3rlpy supports Linux, macOS and Windows.

PyPI

$ pip install d3rlpy

Anaconda

$ conda install -c conda-forge d3rlpy

supported algorithms

algorithm	discrete control	continuous control	data-driven RL?
Behavior Cloning (supervised learning)	:white_check_mark:	:white_check_mark:
Deep Q-Network (DQN)	:white_check_mark:	:no_entry:
Double DQN	:white_check_mark:	:no_entry:
Deep Deterministic Policy Gradients (DDPG)	:no_entry:	:white_check_mark:
Twin Delayed Deep Deterministic Policy Gradients (TD3)	:no_entry:	:white_check_mark:
Soft Actor-Critic (SAC)	:white_check_mark:	:white_check_mark:
Batch Constrained Q-learning (BCQ)	:white_check_mark:	:white_check_mark:	:white_check_mark:
Bootstrapping Error Accumulation Reduction (BEAR)	:no_entry:	:white_check_mark:	:white_check_mark:
Advantage-Weighted Regression (AWR)	:white_check_mark:	:white_check_mark:	:white_check_mark:
Conservative Q-Learning (CQL) (recommended)	:white_check_mark:	:white_check_mark:	:white_check_mark:
Advantage Weighted Actor-Critic (AWAC)	:no_entry:	:white_check_mark:	:white_check_mark:
Policy in Latent Action Space (PLAS)	:no_entry:	:white_check_mark:	:white_check_mark:

supported Q functions

• standard Q function
• Quantile Regression
• Implicit Quantile Network
• Fully parametrized Quantile Function (experimental)

other features

Basically, all features are available with every algorithm.

• evaluation metrics in a scikit-learn scorer function style
• export greedy-policy as TorchScript or ONNX
• ensemble Q function
• N-step TD calculation
• parallel cross validation with multiple GPU
• online training
• data augmentation
• model-based algorithm

examples

Atari 2600

from d3rlpy.datasets import get_atari
from d3rlpy.algos import DiscreteCQL
from d3rlpy.metrics.scorer import evaluate_on_environment
from d3rlpy.metrics.scorer import discounted_sum_of_advantage_scorer
from sklearn.model_selection import train_test_split

# get data-driven RL dataset
dataset, env = get_atari('breakout-expert-v0')

# split dataset
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)

# setup algorithm
cql = DiscreteCQL(n_frames=4, q_func_factory='qr', scaler='pixel', use_gpu=True)

# start training
cql.fit(train_episodes,
        eval_episodes=test_episodes,
        n_epochs=100,
        scorers={
            'environment': evaluate_on_environment(env),
            'advantage': discounted_sum_of_advantage_scorer
        })

See more Atari datasets at d4rl-atari.

PyBullet

from d3rlpy.datasets import get_pybullet
from d3rlpy.algos import CQL
from d3rlpy.metrics.scorer import evaluate_on_environment
from d3rlpy.metrics.scorer import discounted_sum_of_advantage_scorer
from sklearn.model_selection import train_test_split

# get data-driven RL dataset
dataset, env = get_pybullet('hopper-bullet-mixed-v0')

# split dataset
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)

# setup algorithm
cql = CQL(q_func_factory='qr', use_gpu=True)

# start training
cql.fit(train_episodes,
        eval_episodes=test_episodes,
        n_epochs=300,
        scorers={
            'environment': evaluate_on_environment(env),
            'advantage': discounted_sum_of_advantage_scorer
        })

See more PyBullet datasets at d4rl-pybullet.

Online Training

import gym

from d3rlpy.algos import SAC
from d3rlpy.online.buffers import ReplayBuffer

# setup environment
env = gym.make('HopperBulletEnv-v0')
eval_env = gym.make('HopperBulletEnv-v0')

# setup algorithm
sac = SAC(use_gpu=True)

# setup replay buffer
buffer = ReplayBuffer(maxlen=1000000, env=env)

# start training
sac.fit_online(env, buffer, n_steps=1000000, eval_env=eval_env)

tutorials

Try a cartpole example on Google Colaboratory!

scikit-learn compatibility

This library is designed as if born from scikit-learn. You can fully utilize scikit-learn's utilities to increase your productivity.

from sklearn.model_selection import train_test_split
from d3rlpy.metrics.scorer import td_error_scorer

train_episodes, test_episodes = train_test_split(dataset)

cql.fit(train_episodes,
        eval_episodes=test_episodes,
        scorers={'td_error': td_error_scorer})

You can naturally perform cross-validation.

from sklearn.model_selection import cross_validate

scores = cross_validate(cql, dataset, scoring={'td_error': td_error_scorer})

And more.

from sklearn.model_selection import GridSearchCV

gscv = GridSearchCV(estimator=cql,
                    param_grid={'actor_learning_rate': [3e-3, 3e-4, 3e-5]},
                    scoring={'td_error': td_error_scorer},
                    refit=False)
gscv.fit(train_episodes)

MDPDataset

d3rlpy introduces MDPDataset, a convenient data structure for reinforcement learning. MDPDataset splits sequential data into transitions that includes a tuple of data observed at t and t+1, which is usually used for training.

from d3rlpy.dataset import MDPDataset

# offline data
observations = np.random.random((100000, 100)) # 100-dim feature observations
actions = np.random.random((100000, 4)) # 4-dim continuous actions
rewards = np.random.random(100000)
terminals = np.random.randint(2, size=100000)

# builds MDPDataset from offline data
dataset = MDPDataset(observations, actions, rewards, terminals)

# splits offline data into episodes
dataset.episodes[0].observations

# splits episodes into transitions
dataset.episodes[0].transitions[0].observation
dataset.episodes[0].transitions[0].action
dataset.episodes[0].transitions[0].next_reward
dataset.episodes[0].transitions[0].next_observation
dataset.episodes[0].transitions[0].terminal

TransitionMiniBatch is also a convenient class to make a mini-batch of sampled transitions. And, memory copies done in TransitionMiniBatch are implemented with Cython, which provides extremely fast computation.

from random import sample
from d3rlpy.dataset import TransitionMiniBatch

transitions = sample(dataset.episodes[0].transitions, 100)

# fast batching up with efficient-memory copy
batch = TransitionMiniBatch(transitions)

batch.observations.shape == (100, 100)

One more interesting feature in the dataset structure is that each transition has pointers to its next and previous transition. This feature enables JIT frame stacking just as serveral works do with Atari tasks, which is also implemented with Cython for reducing bottlenecks.

observations = np.random.randint(256, size=(100000, 1, 84, 84), dtype=np.uint8) # 1x84x84 pixel images
actions = np.random.randint(4, size=100000) # discrete actions with 4 options
rewards = np.random.random(100000)
terminals = np.random.randint(2, size=100000)

# builds MDPDataset from offline data
dataset = MDPDataset(observations, actions, rewards, terminals, discrete_action=True)

# samples transitions
transitions = sample(dataset.episodes[0].transitions, 32)

# makes mini-batch with frame stacking
batch = TransitionMiniBatch(transitions, n_frames=4)

batch.observations.shape == (32, 4, 84, 84)

Finally, TransitionMiniBatch also supports N-step TD backup, which is also efficiently done with Cython.

batch = TransitionMiniBatch(transitions, n_steps=1, gamma=0.99)

# the number of steps before next observations at each batch index
batch.n_steps.shape == (32, 1)

# N step after batch.observations
batch.next_observations

# N step after batch.actions
batch.next_actions

# N-step return
batch.next_rewards

contributions

coding style

This library is fully formatted with black and yapf. You can format the entire scripts as follows:

$ ./scripts/format

linter

This library is analyzed by mypy and pylint. You can check the code structures as follows:

$ ./scripts/lint

test

The unit tests are provided as much as possible. This repository is using pytest-cov instead of pytest. You can run the entire tests as follows:

$ ./scripts/test

If you give -p option, the performance tests with toy tasks are also run (this will take minutes).

$ ./scripts/test -p

citation

@misc{seno2020d3rlpy,
  author = {Takuma Seno},
  title = {d3rlpy: A data-driven deep reinforcement library as an out-of-the-box tool},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/takuseno/d3rlpy}}
}

acknowledgement

This work is supported by Information-technology Promotion Agency, Japan (IPA), Exploratory IT Human Resources Project (MITOU Program) in the fiscal year 2020.