postrational / gym-demo / 168

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/gym_demo/demo.py

#!/usr/bin/env python

"""Usage: gym_demo.py [--steps=NN --no-render --observations] ENV_NAME

Show a random agent playing in a given OpenAI environment.

Arguments:
  ENV_NAME          Name of the Gym environment to run

Options:
  -h --help
  --steps=<STEPS>   How many iteration to run for.  [default: 5000]
  --no-render       Don't render the environment graphically.
  --observations    Print environment observations.

"""
import re
from time import sleep
from typing import List, Text

import gym
from docopt import docopt

from gym_demo.formatting import list_to_columns, print_error, print_header


def get_environment_names() -> List[Text]:
    """Return a list of names of registered Open AI Gym environments."""
    return sorted(spec.id for spec in gym.envs.registry.all())


def group_environments(env_names: List[Text]) -> List[Text]:
    """Group a sorted list of environment names into families."""
    names_without_version = [name.split("-")[0] for name in env_names]
    family_names = [names_without_version.pop(0)]
    for name in names_without_version:
        if not family_names[-1] in name:
            family_names.append(name)
    return family_names


def get_space_description(space: gym.Space) -> Text:
    """Return a textual description of gym.Space object."""
    description = repr(space)
    if isinstance(space, gym.spaces.Box):
        description += "\nLow values:\n{0}".format(space.low)
        description += "\nHigh values:\n{0}".format(space.high)
    return description


def print_environment_description(env: gym.Env) -> None:
    """Output the Gym environment description to standard out."""
    print_header("Environment: {0}".format(env.spec.id))
    print_header("\nObservation Space:")
    print(get_space_description(env.observation_space))

    print_header("\nAction Space:")
    print(get_space_description(env.action_space))
    if hasattr(env.unwrapped, "get_action_meanings"):
        print("Action meanings:", env.unwrapped.get_action_meanings())
    print("")


def render_environment(env: gym.Env) -> bool:
    """Graphically render state of environment.

    Return true if rendering was successful, false otherwise.
    """
    try:
        env.render()
        sleep(0.02)
        return True
    except NotImplementedError:
        return False


def run_environment(
    env: gym.Env,
    steps_count: int = 1000,
    render: bool = True,
    print_observation: bool = False,
) -> None:
    """Execute main environment run loop.

    Renders environment state graphically and outputs environment information to
    standard out.

    :param env: the environment to run
    :param steps_count: how many steps to run for?
    :param render: should the environment be rendered graphically?
    :param print_observation: should the full observed state be output to std out?
    """
    env.reset()
    print_header("Running environment demonstration...")
    print("Unique environment information is output to standard out:")
    prev_env_output = None
    for step_number in range(steps_count):
        observation, reward, done, info = env.step(env.action_space.sample())

        if render:
            render = render_environment(env)

        if (reward, done, info) != prev_env_output:
            print("Reward: {0}, Done: {1}, Info: {2}".format(reward, done, info))
            prev_env_output = (reward, done, info)

        if print_observation:
            print("Observation: {0}".format(observation))

        if done:
            print("Done after {0} steps.".format(step_number + 1))
            break

    env.close()


def main() -> None:
    """Process script argument and run."""
    environment_names = get_environment_names()
    environment_families = group_environments(environment_names)

    help_string = "{0}\n\nAvailable environments:\n\n{1}".format(
        __doc__, list_to_columns(environment_families)
    )
    arguments = docopt(help_string)

    steps = int(arguments.get("--steps"))
    render_env = not arguments.get("--no-render")
    print_observations = arguments.get("--observations")
    env_name = arguments.get("ENV_NAME")

    if env_name in environment_names:
        environment = gym.make(env_name)
        print_environment_description(environment)
        run_environment(environment, steps, render_env, print_observations)

    else:
        print_error("ERROR: Environment with requested ID not found.")
        regex = re.compile(".*{0}.*".format(env_name), re.IGNORECASE)
        environment_names = [
            spec.id for spec in gym.envs.registry.all() if regex.match(spec.id)
        ]
        if len(environment_names):
            print_header("\nPerhaps you were looking for:")
            print(list_to_columns(environment_names))


if __name__ == "__main__":
    main()

1	#!/usr/bin/env python
2
3	"""Usage: gym_demo.py [--steps=NN --no-render --observations] ENV_NAME	4×
4
5	Show a random agent playing in a given OpenAI environment.
6
7	Arguments:
8	ENV_NAME Name of the Gym environment to run
9
10	Options:
11	-h --help
12	--steps=<STEPS> How many iteration to run for. [default: 5000]
13	--no-render Don't render the environment graphically.
14	--observations Print environment observations.
15
16	"""
17	import re	4×
18	from time import sleep	4×
19	from typing import List, Text	4×
20
21	import gym	4×
22	from docopt import docopt	4×
23
24	from gym_demo.formatting import list_to_columns, print_error, print_header	4×
25
26
27	def get_environment_names() -> List[Text]:	4×
28	"""Return a list of names of registered Open AI Gym environments."""
29	return sorted(spec.id for spec in gym.envs.registry.all())	4×
30
31
32	def group_environments(env_names: List[Text]) -> List[Text]:	4×
33	"""Group a sorted list of environment names into families."""
34	names_without_version = [name.split("-")[0] for name in env_names]	4×
35	family_names = [names_without_version.pop(0)]	4×
36	for name in names_without_version:	4×
37	if not family_names[-1] in name:	4×
38	family_names.append(name)	4×
39	return family_names	4×
40
41
42	def get_space_description(space: gym.Space) -> Text:	4×
43	"""Return a textual description of gym.Space object."""
44	description = repr(space)	4×
45	if isinstance(space, gym.spaces.Box):	4×
46	description += "\nLow values:\n{0}".format(space.low)	4×
47	description += "\nHigh values:\n{0}".format(space.high)	4×
48	return description	4×
49
50
51	def print_environment_description(env: gym.Env) -> None:	4×
52	"""Output the Gym environment description to standard out."""
53	print_header("Environment: {0}".format(env.spec.id))	4×
54	print_header("\nObservation Space:")	4×
55	print(get_space_description(env.observation_space))	4×
56
57	print_header("\nAction Space:")	4×
58	print(get_space_description(env.action_space))	4×
59	if hasattr(env.unwrapped, "get_action_meanings"):	4×
60	print("Action meanings:", env.unwrapped.get_action_meanings())	!
61	print("")	4×
62
63
64	def render_environment(env: gym.Env) -> bool:	4×
65	"""Graphically render state of environment.
66
67	Return true if rendering was successful, false otherwise.
68	"""
69	try:	4×
70	env.render()	4×
71	sleep(0.02)	4×
72	return True	4×
73	except NotImplementedError:	4×
74	return False	4×
75
76
77	def run_environment(	4×
78	env: gym.Env,
79	steps_count: int = 1000,
80	render: bool = True,
81	print_observation: bool = False,
82	) -> None:
83	"""Execute main environment run loop.
84
85	Renders environment state graphically and outputs environment information to
86	standard out.
87
88	:param env: the environment to run
89	:param steps_count: how many steps to run for?
90	:param render: should the environment be rendered graphically?
91	:param print_observation: should the full observed state be output to std out?
92	"""
93	env.reset()	4×
94	print_header("Running environment demonstration...")	4×
95	print("Unique environment information is output to standard out:")	4×
96	prev_env_output = None	4×
97	for step_number in range(steps_count):	4×
98	observation, reward, done, info = env.step(env.action_space.sample())	4×
99
100	if render:	4×
101	render = render_environment(env)	!
102
103	if (reward, done, info) != prev_env_output:	4×
104	print("Reward: {0}, Done: {1}, Info: {2}".format(reward, done, info))	4×
105	prev_env_output = (reward, done, info)	4×
106
107	if print_observation:	4×
108	print("Observation: {0}".format(observation))	4×
109
110	if done:	4×
111	print("Done after {0} steps.".format(step_number + 1))	4×
112	break	4×
113
114	env.close()	4×
115
116
117	def main() -> None:	4×
118	"""Process script argument and run."""
119	environment_names = get_environment_names()	4×
120	environment_families = group_environments(environment_names)	4×
121
122	help_string = "{0}\n\nAvailable environments:\n\n{1}".format(	4×
123	__doc__, list_to_columns(environment_families)
124	)
125	arguments = docopt(help_string)	4×
126
127	steps = int(arguments.get("--steps"))	!
128	render_env = not arguments.get("--no-render")	!
129	print_observations = arguments.get("--observations")	!
130	env_name = arguments.get("ENV_NAME")	!
131
132	if env_name in environment_names:	!
133	environment = gym.make(env_name)	!
134	print_environment_description(environment)	!
135	run_environment(environment, steps, render_env, print_observations)	!
136
137	else:
138	print_error("ERROR: Environment with requested ID not found.")	!
139	regex = re.compile(".{0}.".format(env_name), re.IGNORECASE)	!
140	environment_names = [	!
141	spec.id for spec in gym.envs.registry.all() if regex.match(spec.id)
142	]
143	if len(environment_names):	!
144	print_header("\nPerhaps you were looking for:")	!
145	print(list_to_columns(environment_names))	!
146
147
148	if __name__ == "__main__":	4×
149	main()	!

postrational / gym-demo / 168

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