コード例 #1
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ファイル: mcts_test.py プロジェクト: Danielhp95/Regym
def test_mcts_can_take_actions_discrete_obvservation_discrete_action(
        Connect4Task, mcts_config_dict):
    mcts1 = build_MCTS_Agent(Connect4Task,
                             mcts_config_dict,
                             agent_name='MCTS1-test')
    mcts2 = build_MCTS_Agent(Connect4Task,
                             mcts_config_dict,
                             agent_name='MCTS2-test')
    Connect4Task.run_episode([mcts1, mcts2], training=False)
コード例 #2
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def test_can_defeat_random_play_in_connect4_both_positions_single_env(Connect4Task, expert_iteration_config_dict):
    expert_iteration_config_dict['mcts_budget'] = 100
    expert_iteration_config_dict['mcts_rollout_budget'] = 20
    ex_it = build_ExpertIteration_Agent(Connect4Task, expert_iteration_config_dict, agent_name='MCTS1-test')

    random_agent = build_Random_Agent(Connect4Task, {}, agent_name='Random')

    trajectory = Connect4Task.run_episode([ex_it, random_agent], training=False)
    assert trajectory.winner == 0  # First player (index 0) has a much higher budget

    trajectory = Connect4Task.run_episode([random_agent, ex_it], training=False)
    assert trajectory.winner == 1  # Second player (index 1) has a much higher budget
コード例 #3
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ファイル: mcts_test.py プロジェクト: Mark-F10/Regym
def test_can_defeat_random_play_in_connect4_both_positions(
        Connect4Task, mcts_config_dict):
    mcts1 = build_MCTS_Agent(Connect4Task,
                             mcts_config_dict,
                             agent_name='MCTS1-test')
    mcts_config_dict['budget'] = 50
    mcts2 = build_MCTS_Agent(Connect4Task,
                             mcts_config_dict,
                             agent_name='MCTS2-test')
    trajectory = Connect4Task.run_episode([mcts1, mcts2], training=False)

    assert extract_winner(
        trajectory) == 1  # Second player (index 1) has a much higher budget
    trajectory = Connect4Task.run_episode([mcts2, mcts1], training=False)
    assert extract_winner(
        trajectory) == 0  # First player (index 0) has a much higher budget
コード例 #4
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def test_deterministic_agent_can_act_on_multiagent_sequential_environment(
        Connect4Task):
    expected_actions = [0, 1]
    agent_1 = build_Deterministic_Agent(Connect4Task,
                                        {'action': expected_actions[0]},
                                        'DeterministicTest-1')
    agent_2 = build_Deterministic_Agent(Connect4Task,
                                        {'action': expected_actions[1]},
                                        'DeterministicTest-2')

    trajectory = Connect4Task.run_episode([agent_1, agent_2], training=False)

    for i, (s, a, r, succ_s, o) in enumerate(trajectory):
        assert a == expected_actions[i % 2]
def test_sequential_trajectories_feature_agent_predictions_single_env(
        Connect4Task):
    agent_1 = build_Deterministic_Agent(Connect4Task, {'action': 0},
                                        'Col-0-DeterministicAgent')
    agent_1.requires_opponents_prediction = True  # Required!
    agent_2 = build_Deterministic_Agent(Connect4Task, {'action': 1},
                                        'Col-0-DeterministicAgent')

    trajectory = Connect4Task.run_episode([agent_1, agent_2], training=False)

    expected_prediction_1 = {'a': 0, 'probs': [[1., 0., 0., 0., 0., 0., 0.]]}
    expected_prediction_2 = {'a': 1, 'probs': [[0., 1., 0., 0., 0., 0., 0.]]}
    expected_predictions = [expected_prediction_1, expected_prediction_2]

    compare_trajectory_extra_info_against_expected(trajectory,
                                                   expected_predictions)
コード例 #6
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def test_can_use_apprentice_in_expert_in_expansion_and_rollout_phase(Connect4Task, expert_iteration_config_dict):
    expert_iteration_config_dict['use_apprentice_in_expert'] = True
    expert_iteration_config_dict['rollout_budget'] = 0
    exIt_agent_1 = build_ExpertIteration_Agent(Connect4Task, expert_iteration_config_dict, agent_name='ExIt1-test')
    exIt_agent_2 = build_ExpertIteration_Agent(Connect4Task, expert_iteration_config_dict, agent_name='ExIt2-test')
    Connect4Task.run_episode([exIt_agent_1, exIt_agent_2], training=False)
コード例 #7
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def test_expert_iteration_can_take_actions_discrete_obvservation_discrete_action(Connect4Task, expert_iteration_config_dict):
    exIt_agent_1 = build_ExpertIteration_Agent(Connect4Task, expert_iteration_config_dict, agent_name='ExIt1-test')
    exIt_agent_2 = build_ExpertIteration_Agent(Connect4Task, expert_iteration_config_dict, agent_name='ExIt2-test')
    Connect4Task.run_episode([exIt_agent_1, exIt_agent_2], training=False)