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env_step.py
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# Copyright 2021 Garena Online Private Limited
#
# 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.
import gym
import numpy as np
from packaging import version
import envpool
is_legacy_gym = version.parse(gym.__version__) < version.parse("0.26.0")
def gym_sync_step() -> None:
num_envs = 4
env = envpool.make_gym("Pong-v5", num_envs=num_envs)
action_num = env.action_space.n
if is_legacy_gym:
obs = env.reset() # reset all envs
else:
obs, _ = env.reset() # reset all envs
assert obs.shape == (num_envs, 4, 84, 84)
for _ in range(1000):
# autoreset is automatically enabled in envpool
action = np.random.randint(action_num, size=num_envs)
result = env.step(action)
if is_legacy_gym:
obs, rew, done, info = env.step(action)
else:
obs, rew, term, trunc, info = env.step(action)
# Of course, you can specify env_id to step corresponding envs
if is_legacy_gym:
obs = env.reset(np.array([1, 3])) # reset env #1 and #3
else:
obs, _ = env.reset(np.array([1, 3])) # reset env #1 and #3
assert obs.shape == (2, 4, 84, 84)
partial_action = np.array([0, 0, 2])
env_id = np.array([3, 2, 0])
result = env.step(partial_action, env_id)
obs, info = result[0], result[-1]
np.testing.assert_allclose(info["env_id"], env_id)
assert obs.shape == (3, 4, 84, 84)
def dm_sync_step() -> None:
num_envs = 4
env = envpool.make_dm("Pong-v5", num_envs=num_envs)
action_num = env.action_spec().num_values
ts = env.reset()
# ts.observation is a **NamedTuple** instead of np.ndarray
# because we need to store other valuable information in this field
assert ts.observation.obs.shape, (num_envs, 4, 84, 84)
for _ in range(1000):
# autoreset is automatically enabled in envpool
action = np.random.randint(action_num, size=num_envs)
ts = env.step(action)
# Of course, you can specify env_id to step corresponding envs
ts = env.reset(np.array([1, 3])) # reset env #1 and #3
assert ts.observation.obs.shape == (2, 4, 84, 84)
partial_action = np.array([0, 0, 2])
env_id = np.array([3, 2, 0])
ts = env.step(partial_action, env_id)
np.testing.assert_allclose(ts.observation.env_id, env_id)
assert ts.observation.obs.shape == (3, 4, 84, 84)
def async_step() -> None:
num_envs = 8
batch_size = 4
# Create an envpool that each step only 4 of 8 result will be out,
# and left other "slow step" envs execute at background.
env = envpool.make_dm("Pong-v5", num_envs=num_envs, batch_size=batch_size)
action_num = env.action_spec().num_values
ts = env.reset()
for _ in range(1000):
env_id = ts.observation.env_id
assert len(env_id) == batch_size
# generate action with len(action) == len(env_id)
action = np.random.randint(action_num, size=batch_size)
ts = env.step(action, env_id)
# Same as gym
env = envpool.make_gym(
"Pong-v5",
num_envs=num_envs,
batch_size=batch_size,
gym_reset_return_info=True,
)
# If you want gym's reset() API return env_id,
# just set gym_reset_return_info=True
obs, info = env.reset()
assert obs.shape == (batch_size, 4, 84, 84)
env_id = info["env_id"]
for _ in range(1000):
action = np.random.randint(action_num, size=batch_size)
result = env.step(action, env_id)
obs, info = result[0], result[-1]
env_id = info["env_id"]
assert len(env_id) == batch_size
assert obs.shape == (batch_size, 4, 84, 84)
# We can also use a low-level API (send and recv)
env = envpool.make_gym("Pong-v5", num_envs=num_envs, batch_size=batch_size)
env.async_reset() # no return, just send `reset` signal to all envs
for _ in range(1000):
result = env.recv()
obs, info = result[0], result[-1]
env_id = info["env_id"]
assert len(env_id) == batch_size
assert obs.shape == (batch_size, 4, 84, 84)
action = np.random.randint(action_num, size=batch_size)
env.send(action, env_id)
if __name__ == "__main__":
gym_sync_step()
dm_sync_step()
async_step()