83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
# Copyright (C) 2020-2025 Motphys Technology Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import gymnasium as gym
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import numpy as np
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from absl import app, flags
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from motrix_envs import registry
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from motrix_envs.np.env import NpEnv
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from motrix_envs.np.renderer import NpRenderer
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_ENV = flags.DEFINE_string("env", "cartpole", "The env to view")
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_SIM_BACKEND = flags.DEFINE_string("sim-backend", None, "The simulation backend to use.")
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_NUM_ENVS = flags.DEFINE_integer("num-envs", 1, "Number of parallel environments.")
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class NpEnvRunner:
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_renderer: NpRenderer
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def __init__(self, env: NpEnv):
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self._env = env
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self._renderer = NpRenderer(env)
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def _sample_random_action(self):
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action_space = self._env.action_space
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if isinstance(action_space, gym.spaces.Box):
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size = (self._env.num_envs, *action_space.shape)
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low = action_space.low
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high = action_space.high
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low = np.where(np.isneginf(low), -1e6, low)
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high = np.where(np.isposinf(high), 1e6, high)
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return np.random.uniform(
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low=low,
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high=high,
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size=size,
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).astype(action_space.dtype)
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else:
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raise NotImplementedError("Only Box action space is supported")
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def step(self):
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actions = self._sample_random_action()
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self._env.step(actions)
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def start(self):
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import time
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env_dt = self._env.cfg.ctrl_dt
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while True:
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t0 = time.monotonic()
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actions = self._sample_random_action()
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self._env.step(actions)
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self._renderer.render()
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real_dt = time.monotonic() - t0
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sleep_dt = env_dt - real_dt
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if sleep_dt > 0:
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time.sleep(sleep_dt)
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def main(argv):
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env_name = _ENV.value
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sim_backend = _SIM_BACKEND.value
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num_envs = _NUM_ENVS.value
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env = registry.make(env_name, sim_backend=sim_backend, num_envs=num_envs)
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runner = NpEnvRunner(env)
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runner.start()
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if __name__ == "__main__":
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app.run(main)
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