def test_add_experiennce(p_exp_handler: PrioritizedExperienceHandler):
for add in range(3):
state = np.ones((2, 10, 10)) * add
action = add
reward = add
p_exp_handler.add_experience(state, action, reward)
assert p_exp_handler.tree.root is not None
assert p_exp_handler.tree.root.value == np.inf
assert p_exp_handler.tree.root.extra_vals == 0
class PrioritizedExperienceLearner(learner):
def __init__(self, skip_frame, num_actions, load=None):
super().__init__()
rand_vals = (1, 0.1, 10000 / skip_frame) # starting at 1 anneal eGreedy policy to 0.1 over 1,000,000/skip_frame
self.action_handler = ActionHandler(ActionPolicy.eGreedy, rand_vals)
self.exp_handler = PrioritizedExperienceHandler(1000000 / skip_frame)
self.train_handler = TrainHandler(32, num_actions)
self.cnn = CNN((None, skip_frame, 86, 80), num_actions, 0.1)
self.discount = 0.99
if load is not None:
self.cnn.load(load)
def frames_processed(self, frames, action_performed, reward):
self.exp_handler.add_experience(frames, self.action_handler.game_action_to_action_ind(action_performed), reward)
self.train_handler.train_prioritized(self.exp_handler, 0.99, self.cnn)
self.action_handler.anneal()
def plot_tree(self):
self.exp_handler.tree.plot()
def get_action(self, game_input):
return self.cnn.get_output(game_input)[0]
def game_over(self):
self.exp_handler.trim() # trim experience replay of learner
self.exp_handler.add_terminal() # adds a terminal
def get_game_action(self, game_input):
return self.action_handler.action_vect_to_game_action(self.get_action(game_input))
def set_legal_actions(self, legal_actions):
self.action_handler.set_legal_actions(legal_actions)
def save(self, file):
self.cnn.save(file)
def get_cost_list(self):
return self.train_handler.costList
def test_trim(p_exp_handler: PrioritizedExperienceHandler):
old_state_size = len(p_exp_handler.states)
p_exp_handler.trim()
assert p_exp_handler.size < old_state_size
assert len(p_exp_handler.states) < old_state_size
assert p_exp_handler.size == p_exp_handler.max_len
assert len(p_exp_handler.states) == p_exp_handler.max_len
assert len(p_exp_handler.states) == p_exp_handler.size
assert list(p_exp_handler.term_states)[0] == p_exp_handler.size - 1
# check tree extra_vals have been updated
assert p_exp_handler.tree.get_size() == p_exp_handler.size
# 0 should still be there because it will still be as inf
assert p_exp_handler.tree.root.value == np.inf
assert p_exp_handler.tree.root.extra_vals == 0
# 2 should be the only one left and it's ind should now be 1
assert p_exp_handler.tree.root.left.value == 2
assert p_exp_handler.tree.root.left.extra_vals == 1
# check term_states is still a set
assert isinstance(p_exp_handler.term_states, set)
# Test that terminal states that have been deleted are removed
# add new states and trim
for add in range(1):
state = np.ones((2, 10, 10)) * add
action = add
reward = add
p_exp_handler.add_experience(state, action, reward)
p_exp_handler.trim()
# the left value should be deleted
assert p_exp_handler.tree.root.left is None
# right value should be 1 because we deleted state 1 (and it used to be 2)
assert p_exp_handler.tree.root.right.extra_vals == 1
assert len(list(p_exp_handler.term_states)) == 0
