envs/fb_mtenv_dmc/__init__.py

# Copyright 2017 The dm_control Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

"""A collection of MuJoCo-based Reinforcement Learning environments."""

from __future__ import absolute_import, division, print_function

import collections
import inspect
import itertools
import sys

from dm_control.rl import control

from . import (
    acrobot,
    ball_in_cup,
    cartpole,
    cheetah,
    finger,
    fish,
    hopper,
    humanoid,
    humanoid_CMU,
    lqr,
    manipulator,
    pendulum,
    point_mass,
    quadruped,
    reacher,
    stacker,
    swimmer,
    walker,
)

# Find all domains imported.
_DOMAINS = {
    name: module
    for name, module in locals().items()
    if inspect.ismodule(module) and hasattr(module, "SUITE")
}


def _get_tasks(tag):
    """Returns a sequence of (domain name, task name) pairs for the given tag."""
    result = []

    for domain_name in sorted(_DOMAINS.keys()):
        domain = _DOMAINS[domain_name]

        if tag is None:
            tasks_in_domain = domain.SUITE
        else:
            tasks_in_domain = domain.SUITE.tagged(tag)

        for task_name in tasks_in_domain.keys():
            result.append((domain_name, task_name))

    return tuple(result)


def _get_tasks_by_domain(tasks):
    """Returns a dict mapping from task name to a tuple of domain names."""
    result = collections.defaultdict(list)

    for domain_name, task_name in tasks:
        result[domain_name].append(task_name)

    return {k: tuple(v) for k, v in result.items()}


# A sequence containing all (domain name, task name) pairs.
ALL_TASKS = _get_tasks(tag=None)

# Subsets of ALL_TASKS, generated via the tag mechanism.
BENCHMARKING = _get_tasks("benchmarking")
EASY = _get_tasks("easy")
HARD = _get_tasks("hard")
EXTRA = tuple(sorted(set(ALL_TASKS) - set(BENCHMARKING)))

# A mapping from each domain name to a sequence of its task names.
TASKS_BY_DOMAIN = _get_tasks_by_domain(ALL_TASKS)


def load(
    domain_name,
    task_name,
    task_kwargs=None,
    environment_kwargs=None,
    visualize_reward=False,
):
    """Returns an environment from a domain name, task name and optional settings.

    ```python
    env = suite.load('cartpole', 'balance')
    ```

    Args:
      domain_name: A string containing the name of a domain.
      task_name: A string containing the name of a task.
      task_kwargs: Optional `dict` of keyword arguments for the task.
      environment_kwargs: Optional `dict` specifying keyword arguments for the
        environment.
      visualize_reward: Optional `bool`. If `True`, object colours in rendered
        frames are set to indicate the reward at each step. Default `False`.

    Returns:
      The requested environment.
    """
    return build_environment(
        domain_name, task_name, task_kwargs, environment_kwargs, visualize_reward,
    )


def build_environment(
    domain_name,
    task_name,
    task_kwargs=None,
    environment_kwargs=None,
    visualize_reward=False,
):
    """Returns an environment from the suite given a domain name and a task name.

    Args:
      domain_name: A string containing the name of a domain.
      task_name: A string containing the name of a task.
      task_kwargs: Optional `dict` specifying keyword arguments for the task.
      environment_kwargs: Optional `dict` specifying keyword arguments for the
        environment.
      visualize_reward: Optional `bool`. If `True`, object colours in rendered
        frames are set to indicate the reward at each step. Default `False`.

    Raises:
      ValueError: If the domain or task doesn't exist.

    Returns:
      An instance of the requested environment.
    """
    if domain_name not in _DOMAINS:
        raise ValueError("Domain {!r} does not exist.".format(domain_name))

    domain = _DOMAINS[domain_name]

    if task_name not in domain.SUITE:
        raise ValueError(
            "Level {!r} does not exist in domain {!r}.".format(task_name, domain_name)
        )

    task_kwargs = task_kwargs or {}
    if environment_kwargs is not None:
        task_kwargs = task_kwargs.copy()
        task_kwargs["environment_kwargs"] = environment_kwargs
    env = domain.SUITE[task_name](**task_kwargs)
    env.task.visualize_reward = visualize_reward
    return env