def test_egreedy_select_action_exploitation(): trial_num = 50 policy = EpsilonGreedy(n_actions=2, epsilon=0.0) policy.action_counts = np.array([3, 3]) policy.reward_counts = np.array([3, 0]) for _ in range(trial_num): assert policy.select_action()[0] == 0
def test_egreedy_select_action_exploration(): trial_num = 50 policy = EpsilonGreedy(n_actions=2, epsilon=1.0) policy.action_counts = np.array([3, 3]) policy.reward_counts = np.array([3, 0]) selected_action = [policy.select_action() for _ in range(trial_num)] assert 0 < sum(selected_action)[0] < trial_num
def test_egreedy_update_params(): policy = EpsilonGreedy(n_actions=2, epsilon=1.0) policy.action_counts_temp = np.array([4, 3]) policy.action_counts = np.copy(policy.action_counts_temp) policy.reward_counts_temp = np.array([2.0, 0.0]) policy.reward_counts = np.copy(policy.reward_counts_temp) action = 0 reward = 1.0 policy.update_params(action, reward) assert np.array_equal(policy.action_counts, np.array([5, 3])) assert np.allclose(policy.reward_counts, np.array([2.0 + reward, 0.0]))
def test_egreedy_update_params(): policy = EpsilonGreedy(n_actions=2, epsilon=1.0) policy.action_counts_temp = np.array([4, 3]) policy.action_counts = np.copy(policy.action_counts_temp) policy.reward_counts_temp = np.array([2.0, 0.0]) policy.reward_counts = np.copy(policy.reward_counts_temp) action = 0 reward = 1.0 policy.update_params(action, reward) assert np.array_equal(policy.action_counts, np.array([5, 3])) # in epsilon greedy, reward_counts is defined as the mean of observed rewards for each action next_reward = (2.0 * (5 - 1) / 5) + (reward / 5) assert np.allclose(policy.reward_counts, np.array([next_reward, 0.0]))