"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),
},
# ======================================================================= #
# Point navigation environments. #
# ======================================================================= #
"Point2DEnv": {
"meta_ac_space":
lambda relative_goals, multiagent: Box(
np.ones(2) * -4, np.ones(2) * 4, dtype=np.float32),
"state_indices":
lambda multiagent: [0, 1],
"env":
lambda evaluate, render, n_levels, multiagent, shared, maddpg:
Point2DEnv(images_in_obs=False),
},
.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]
]),
dtype=np.float32),
"state_indices":
[i + 1024 for i in [0, 4, 5, 6, 7, 32, 33, 34, 50, 51, 52]],
"env":
lambda evaluate, render, multiagent, shared, maddpg: BipedalObstacles(
render=render),
},
# ======================================================================= #
# Point navigation environments. #
# ======================================================================= #
"Point2DEnv": {
"meta_ac_space":
lambda relative_goals: Box(
np.ones(2) * -4, np.ones(2) * 4, dtype=np.float32),
"state_indices": [0, 1],
"env":
lambda evaluate, render, multiagent, shared, maddpg: Point2DEnv(
images_in_obs=False),
},
"Point2DImageEnv": {