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Build # 1494

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Pull #132

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web-flow

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<a href="https://github.com/AboudyKreidieh/h-baselines/commit/<a class=hub.com/AboudyKreidieh/h-baselines/commit/80f819e4b86910e54fce45a3041e141fd7528dda">80f819e4b<a href="https://github.com/AboudyKreidieh/h-baselines/commit/80f819e4b86910e54fce45a3041e141fd7528dda">">Merge </a><a class="double-link" href="https://github.com/AboudyKreidieh/h-baselines/commit/<a class="double-link" href="https://github.com/AboudyKreidieh/h-baselines/commit/236b04ca5fc31dd9931143f061c44f8880b3e9d3">236b04ca5</a>">236b04ca5</a><a href="https://github.com/AboudyKreidieh/h-baselines/commit/80f819e4b86910e54fce45a3041e141fd7528dda"> into f1ba27380">f1ba27380</a>

Pull Request Pull Request #132: Highway single

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/hbaselines/envs/mixed_autonomy/core.py

"""Script containing environment generator for modified Flow benchmarks."""
import gym
from gym.spaces import Box
from copy import deepcopy
import numpy as np
from flow.utils.registry import make_create_env


class FlowEnv(gym.Env):
    """Create a flow-specific environment, as provided by this repository.

    The original environments are wrapped by an additional environment that
    accounts for return done values of True when the time horizon is met.

    Attributes
    ----------
    wrapped_env : flow.envs.Env
        the original Flow environment
    step_number : int
        number of steps taken in the current rollout
    horizon : int
        the environment's time horizon
    """

    def __init__(self,
                 flow_params,
                 multiagent=False,
                 shared=False,
                 maddpg=False,
                 render=False,
                 version=0):
        """Create the environment.

        Parameters
        ----------
        flow_params : dict
            environment-specific parameters
        multiagent : bool
            whether the environment is a multi-agent environment
        shared : bool
            whether the policies in the environment are shared or independent.
            This is only relevant if `shared` is set to True.
        render : bool
            whether to render the environment
        version : int
            environment version number, needed for testing purposes
        """
        # Initialize some variables.
        self.multiagent = multiagent
        self.shared = shared
        self.maddpg = maddpg

        if "full_observation_fn" in flow_params["env"].additional_params:
            self.full_observation_fn = deepcopy(
                flow_params["env"].additional_params["full_observation_fn"])
            del flow_params["env"].additional_params["full_observation_fn"]
        else:
            self.full_observation_fn = None

        # Create the wrapped environment.
        create_env, _ = make_create_env(flow_params, version, render)
        self.wrapped_env = create_env()

        # Collect the IDs of individual vehicles if using a multi-agent env.
        if self.multiagent:
            self.agents = list(self.wrapped_env.reset().keys())

        # for tracking the time horizon
        self.step_number = 0
        self.horizon = self.wrapped_env.env_params.horizon

    @property
    def action_space(self):
        """See wrapped environment."""
        if self.multiagent and not self.shared:
            return {key: self.wrapped_env.action_space for key in self.agents}
        else:
            return self.wrapped_env.action_space

    @property
    def observation_space(self):
        """See wrapped environment."""
        if self.multiagent and not self.shared:
            return {
                key: self.wrapped_env.observation_space for key in self.agents}
        else:
            return self.wrapped_env.observation_space

    def step(self, action):
        """See wrapped environment.

        The done term is also modified in case the time horizon is met.
        """
        obs, reward, done, info_dict = self.wrapped_env.step(action)

        # Check if the time horizon has been met.
        self.step_number += 1
        done = done or self.step_number == self.horizon

        # Add the full-state observation, if needed.
        if self.maddpg:
            obs = {
                "obs": obs,
                "all_obs": np.asarray(
                    [self.full_observation_fn(self.wrapped_env)])
            }

        return obs, reward, done, info_dict

    def reset(self):
        """Reset the environment."""
        self.step_number = 0

        obs = self.wrapped_env.reset()

        # Add the full-state observation, if needed.
        if self.maddpg:
            obs = {
                "obs": obs,
                "all_obs": np.asarray(
                    [self.full_observation_fn(self.wrapped_env)])
            }

        return obs

    @property
    def all_observation_space(self):
        """Return the shape of the full observation space."""
        if self.full_observation_fn is None:
            return None
        else:
            return Box(
                low=-float("inf"),
                high=float("inf"),
                shape=self.full_observation_fn(self.wrapped_env).shape,
                dtype=np.float32,
            )

    def render(self, mode='human'):
        """See parent class."""
        pass

1	"""Script containing environment generator for modified Flow benchmarks."""
2	import gym	1✔
3	from gym.spaces import Box	1✔
4	from copy import deepcopy	1✔
5	import numpy as np	1✔
6	from flow.utils.registry import make_create_env	1✔
7
8
9	class FlowEnv(gym.Env):	1✔
10	"""Create a flow-specific environment, as provided by this repository.
11
12	The original environments are wrapped by an additional environment that
13	accounts for return done values of True when the time horizon is met.
14
15	Attributes
16	----------
17	wrapped_env : flow.envs.Env
18	the original Flow environment
19	step_number : int
20	number of steps taken in the current rollout
21	horizon : int
22	the environment's time horizon
23	"""
24
25	def __init__(self,	1✔
26	flow_params,
27	multiagent=False,
28	shared=False,
29	maddpg=False,
30	render=False,
31	version=0):
32	"""Create the environment.
33
34	Parameters
35	----------
36	flow_params : dict
37	environment-specific parameters
38	multiagent : bool
39	whether the environment is a multi-agent environment
40	shared : bool
41	whether the policies in the environment are shared or independent.
42	This is only relevant if `shared` is set to True.
43	render : bool
44	whether to render the environment
45	version : int
46	environment version number, needed for testing purposes
47	"""
48	# Initialize some variables.
49	self.multiagent = multiagent	1✔
50	self.shared = shared	1✔
51	self.maddpg = maddpg	1✔
52
53	if "full_observation_fn" in flow_params["env"].additional_params:	1✔
54	self.full_observation_fn = deepcopy(	1✔
55	flow_params["env"].additional_params["full_observation_fn"])
56	del flow_params["env"].additional_params["full_observation_fn"]	1✔
57	else:
58	self.full_observation_fn = None	1✔
59
60	# Create the wrapped environment.
61	create_env, _ = make_create_env(flow_params, version, render)	1✔
62	self.wrapped_env = create_env()	1✔
63
64	# Collect the IDs of individual vehicles if using a multi-agent env.
65	if self.multiagent:	1✔
66	self.agents = list(self.wrapped_env.reset().keys())	1✔
67
68	# for tracking the time horizon
69	self.step_number = 0	1✔
70	self.horizon = self.wrapped_env.env_params.horizon	1✔
71
72	@property	1✔
73	def action_space(self):
74	"""See wrapped environment."""
75	if self.multiagent and not self.shared:	1✔
76	return {key: self.wrapped_env.action_space for key in self.agents}	1✔
77	else:
78	return self.wrapped_env.action_space	1✔
79
80	@property	1✔
81	def observation_space(self):
82	"""See wrapped environment."""
83	if self.multiagent and not self.shared:	1✔
84	return {	1✔
85	key: self.wrapped_env.observation_space for key in self.agents}
86	else:
87	return self.wrapped_env.observation_space	1✔
88
89	def step(self, action):	1✔
90	"""See wrapped environment.
91
92	The done term is also modified in case the time horizon is met.
93	"""
94	obs, reward, done, info_dict = self.wrapped_env.step(action)	1✔
95
96	# Check if the time horizon has been met.
97	self.step_number += 1	1✔
98	done = done or self.step_number == self.horizon	1✔
99
100	# Add the full-state observation, if needed.
101	if self.maddpg:	1✔
102	obs = {	1✔
103	"obs": obs,
104	"all_obs": np.asarray(
105	[self.full_observation_fn(self.wrapped_env)])
106	}
107
108	return obs, reward, done, info_dict	1✔
109
110	def reset(self):	1✔
111	"""Reset the environment."""
112	self.step_number = 0	1✔
113
114	obs = self.wrapped_env.reset()	1✔
115
116	# Add the full-state observation, if needed.
117	if self.maddpg:	1✔
118	obs = {	1✔
119	"obs": obs,
120	"all_obs": np.asarray(
121	[self.full_observation_fn(self.wrapped_env)])
122	}
123
124	return obs	1✔
125
126	@property	1✔
127	def all_observation_space(self):
128	"""Return the shape of the full observation space."""
129	if self.full_observation_fn is None:	1✔
130	return None	×
131	else:
132	return Box(	1✔
133	low=-float("inf"),
134	high=float("inf"),
135	shape=self.full_observation_fn(self.wrapped_env).shape,
136	dtype=np.float32,
137	)
138
139	def render(self, mode='human'):	1✔
140	"""See parent class."""
141	pass	×

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