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82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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import pytest
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from rl_coach.spaces import DiscreteActionSpace
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from rl_coach.exploration_policies.e_greedy import EGreedy
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from rl_coach.schedules import LinearSchedule
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import numpy as np
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from rl_coach.core_types import RunPhase
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@pytest.mark.unit_test
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def test_get_action():
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# discrete control
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action_space = DiscreteActionSpace(3)
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epsilon_schedule = LinearSchedule(1.0, 1.0, 1000)
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policy = EGreedy(action_space, epsilon_schedule, evaluation_epsilon=0)
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# verify that test phase gives greedy actions (evaluation_epsilon = 0)
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policy.change_phase(RunPhase.TEST)
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for i in range(100):
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best_action, _ = policy.get_action(np.array([10, 20, 30]))
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assert best_action == 2
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# verify that train phase gives uniform actions (exploration = 1)
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policy.change_phase(RunPhase.TRAIN)
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counters = np.array([0, 0, 0])
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for i in range(30000):
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best_action, _ = policy.get_action(np.array([10, 20, 30]))
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counters[best_action] += 1
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assert np.all(counters > 9500) # this is noisy so we allow 5% error
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# TODO: test continuous actions
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@pytest.mark.unit_test
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def test_change_phase():
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# discrete control
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action_space = DiscreteActionSpace(3)
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epsilon_schedule = LinearSchedule(1.0, 0.1, 1000)
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policy = EGreedy(action_space, epsilon_schedule, evaluation_epsilon=0.01)
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# verify schedule not applying if not in training phase
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assert policy.get_control_param() == 1.0
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policy.change_phase(RunPhase.TEST)
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best_action = policy.get_action(np.array([10, 20, 30]))
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assert policy.epsilon_schedule.current_value == 1.0
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policy.change_phase(RunPhase.HEATUP)
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best_action = policy.get_action(np.array([10, 20, 30]))
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assert policy.epsilon_schedule.current_value == 1.0
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policy.change_phase(RunPhase.UNDEFINED)
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best_action = policy.get_action(np.array([10, 20, 30]))
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assert policy.epsilon_schedule.current_value == 1.0
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@pytest.mark.unit_test
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def test_get_control_param():
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# discrete control
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action_space = DiscreteActionSpace(3)
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epsilon_schedule = LinearSchedule(1.0, 0.1, 1000)
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policy = EGreedy(action_space, epsilon_schedule, evaluation_epsilon=0.01)
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# verify schedule applies to TRAIN phase
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policy.change_phase(RunPhase.TRAIN)
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for i in range(999):
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best_action = policy.get_action(np.array([10, 20, 30]))
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assert 1.0 > policy.get_control_param() > 0.1
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best_action = policy.get_action(np.array([10, 20, 30]))
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assert policy.get_control_param() == 0.1
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# test phases
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policy.change_phase(RunPhase.TEST)
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assert policy.get_control_param() == 0.01
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policy.change_phase(RunPhase.TRAIN)
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assert policy.get_control_param() == 0.1
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policy.change_phase(RunPhase.HEATUP)
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assert policy.get_control_param() == 0.1
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