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55 行
1.8 KiB
55 行
1.8 KiB
from mlagents_envs.registry import default_registry
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from mlagents_envs.side_channel.engine_configuration_channel import (
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EngineConfigurationChannel,
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)
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from mlagents_envs.base_env import ActionTuple
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import numpy as np
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BALL_ID = "3DBall"
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def test_set_action_single_agent():
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engine_config_channel = EngineConfigurationChannel()
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env = default_registry[BALL_ID].make(
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base_port=6000,
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worker_id=0,
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no_graphics=True,
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side_channels=[engine_config_channel],
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)
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engine_config_channel.set_configuration_parameters(time_scale=100)
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for _ in range(3):
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env.reset()
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behavior_name = list(env.behavior_specs.keys())[0]
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d, t = env.get_steps(behavior_name)
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for _ in range(50):
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for agent_id in d.agent_id:
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action = np.ones((1, 2))
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action_tuple = ActionTuple()
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action_tuple.add_continuous(action)
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env.set_action_for_agent(behavior_name, agent_id, action_tuple)
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env.step()
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d, t = env.get_steps(behavior_name)
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env.close()
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def test_set_action_multi_agent():
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engine_config_channel = EngineConfigurationChannel()
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env = default_registry[BALL_ID].make(
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base_port=6001,
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worker_id=0,
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no_graphics=True,
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side_channels=[engine_config_channel],
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)
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engine_config_channel.set_configuration_parameters(time_scale=100)
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for _ in range(3):
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env.reset()
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behavior_name = list(env.behavior_specs.keys())[0]
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d, t = env.get_steps(behavior_name)
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for _ in range(50):
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action = np.ones((len(d), 2))
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action_tuple = ActionTuple()
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action_tuple.add_continuous(action)
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env.set_actions(behavior_name, action_tuple)
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env.step()
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d, t = env.get_steps(behavior_name)
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env.close()
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