# ======================================================================= #
# Bipedal environments. #
# ======================================================================= #
"BipedalSoccer": {
"meta_ac_space":
lambda relative_goals, multiagent: Box(
low=np.array(
[-0.5, -1, -1, -1, -1, -2, -2, -2, -2, -2, -2, -2, -1, -2]),
high=np.array([0.5, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2]),
dtype=np.float32),
"state_indices":
lambda multiagent: [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52, 57, 58, 59],
"env":
lambda evaluate, render, n_levels, multiagent, shared, maddpg:
BipedalSoccer(render=render),
},
"BipedalObstacles": {
"meta_ac_space":
lambda relative_goals, multiagent: gym.spaces.Box(
low=np.array([0, -1, -1, -1, -1, -2, -2, -2, -2, -2, -2]),
high=np.array([1.5, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]),
dtype=np.float32),
"state_indices":
lambda multiagent:
[i + 1024 for i in [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52]],
"env":
lambda evaluate, render, n_levels, multiagent, shared, maddpg:
BipedalObstacles(render=render),
},
def create_env(env, render=False, evaluate=False):
"""Return, and potentially create, the environment.
Parameters
----------
env : str or gym.Env
the environment, or the name of a registered environment.
render : bool
whether to render the environment
evaluate : bool
specifies whether this is a training or evaluation environment
Returns
-------
gym.Env or list of gym.Env
gym-compatible environment(s)
"""
if env == "AntGather":
env = AntGatherEnv()
elif env == "AntMaze":
if evaluate:
env = [
AntMaze(use_contexts=True, context_range=[16, 0]),
AntMaze(use_contexts=True, context_range=[16, 16]),
AntMaze(use_contexts=True, context_range=[0, 16])
]
else:
env = AntMaze(use_contexts=True,
random_contexts=True,
context_range=[(-4, 20), (-4, 20)])
elif env == "AntPush":
if evaluate:
env = AntPush(use_contexts=True, context_range=[0, 19])
else:
env = AntPush(use_contexts=True, context_range=[0, 19])
# env = AntPush(use_contexts=True,
# random_contexts=True,
# context_range=[(-16, 16), (-4, 20)])
elif env == "AntFall":
if evaluate:
env = AntFall(use_contexts=True, context_range=[0, 27, 4.5])
else:
env = AntFall(use_contexts=True, context_range=[0, 27, 4.5])
# env = AntFall(use_contexts=True,
# random_contexts=True,
# context_range=[(-4, 12), (-4, 28), (0, 5)])
elif env == "AntFourRooms":
if evaluate:
env = [
AntFourRooms(use_contexts=True, context_range=[30, 0]),
AntFourRooms(use_contexts=True, context_range=[0, 30]),
AntFourRooms(use_contexts=True, context_range=[30, 30])
]
else:
env = AntFourRooms(use_contexts=True,
random_contexts=False,
context_range=[[30, 0], [0, 30], [30, 30]])
elif env == "UR5":
if evaluate:
env = UR5(use_contexts=True,
random_contexts=True,
context_range=[(-np.pi, np.pi), (-np.pi / 4, 0),
(-np.pi / 4, np.pi / 4)],
show=render)
else:
env = UR5(use_contexts=True,
random_contexts=True,
context_range=[(-np.pi, np.pi), (-np.pi / 4, 0),
(-np.pi / 4, np.pi / 4)],
show=render)
elif env == "Pendulum":
if evaluate:
env = Pendulum(use_contexts=True,
context_range=[0, 0],
show=render)
else:
env = Pendulum(use_contexts=True,
random_contexts=True,
context_range=[(np.deg2rad(-16), np.deg2rad(16)),
(-0.6, 0.6)],
show=render)
elif env in [
"bottleneck0", "bottleneck1", "bottleneck2", "grid0", "grid1"
]:
# Import the benchmark and fetch its flow_params
benchmark = __import__("flow.benchmarks.{}".format(env),
fromlist=["flow_params"])
flow_params = benchmark.flow_params
# Get the env name and a creator for the environment.
create_env, _ = make_create_env(flow_params, version=0, render=render)
# Create the environment.
env = create_env()
elif env in ["ring0", "multi-ring0"]:
env = FlowEnv("ring", render=render) # FIXME
elif env in [
"merge0", "merge1", "merge2", "multi-merge0", "multi-merge1",
"multi-merge2"
]:
env_num = int(env[-1])
env = FlowEnv("merge",
env_params={
"exp_num": env_num,
"horizon": 6000,
"simulator": "traci",
"multiagent": env[:5] == "multi"
},
render=render)
elif env in [
"figureeight0", "figureeight1", "figureeight02",
"multi-figureeight0", "multi-figureeight1", "multi-figureeight02"
]:
env_num = int(env[-1])
env = FlowEnv("figure_eight",
env_params={
"num_automated": [1, 7, 14][env_num],
"horizon": 750,
"simulator": "traci",
"multiagent": env[:5] == "multi"
},
render=render)
elif env == "BipedalSoccer":
env = BipedalSoccer(render=render)
elif isinstance(env, str):
# This is assuming the environment is registered with OpenAI gym.
env = gym.make(env)
# Reset the environment.
if env is not None:
if isinstance(env, list):
for next_env in env:
next_env.reset()
else:
env.reset()
return env
),
},
# ======================================================================= #
# Bipedal environments. #
# ======================================================================= #
"BipedalSoccer": {
"meta_ac_space":
lambda relative_goals: Box(
low=np.array(
[-0.5, -1, -1, -1, -1, -2, -2, -2, -2, -2, -2, -2, -1, -2]),
high=np.array([0.5, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2]),
dtype=np.float32),
"state_indices": [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52, 57, 58, 59],
"env":
lambda evaluate, render, multiagent, shared, maddpg: BipedalSoccer(
render=render),
},
"BipedalObstacles": {
"meta_ac_space":
lambda relative_goals: gym.spaces
.Box(low=np.
array([
x
for i, x in enumerate(BipedalObstacles.observation_space.low)
if i - 1024 in [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52]
]),
high=np.array([
x
for i, x in enumerate(BipedalObstacles.observation_space.high)
if i - 1024 in [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52]
]),
# ======================================================================= #
# Bipedal environments. #
# ======================================================================= #
"BipedalSoccer": {
"meta_ac_space":
lambda relative_goals, multiagent: Box(
low=np.array(
[-0.5, -1, -1, -1, -1, -2, -2, -2, -2, -2, -2, -2, -1, -2]),
high=np.array([0.5, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2]),
dtype=np.float32),
"state_indices":
lambda multiagent: [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52, 57, 58, 59],
"env":
lambda evaluate, render, n_levels, multiagent, shared, maddpg:
BipedalSoccer(),
},
"BipedalObstacles": {
"meta_ac_space":
lambda relative_goals, multiagent: gym.spaces.Box(
low=np.array([0, -1, -1, -1, -1, -2, -2, -2, -2, -2, -2]),
high=np.array([1.5, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]),
dtype=np.float32),
"state_indices":
lambda multiagent:
[i + 1024 for i in [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52]],
"env":
lambda evaluate, render, n_levels, multiagent, shared, maddpg:
BipedalObstacles(render=render),
},