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40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
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import numpy as np
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from typing import List
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from rl_coach.core_types import ActionType
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from rl_coach.spaces import ActionSpace
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from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
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class MyExplorationPolicy(ExplorationPolicy):
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"""
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An exploration policy takes the predicted actions or action values from the agent, and selects the action to
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actually apply to the environment using some predefined algorithm.
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"""
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def __init__(self, action_space: ActionSpace):
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#self.phase = RunPhase.HEATUP
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self.action_space = action_space
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super().__init__(action_space)
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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if (np.random.rand() < 0.5):
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chosen_action = self.action_space.sample()
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else:
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chosen_action = np.argmax(action_values)
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probabilities = np.zeros(len(self.action_space.actions))
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probabilities[chosen_action] = 1
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return chosen_action, probabilities
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def get_control_param(self):
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return 0
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class MyExplorationParameters(ExplorationParameters):
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def __init__(self):
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super().__init__()
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@property
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def path(self):
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return 'exploration:MyExplorationPolicy'
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