def test_get_prioritized_experience(p_exp_handler: PrioritizedExperienceHandler):
assert len(list(p_exp_handler.term_states)) > 0 # assert there is a terminal state
states, _, _, _, _, _ = p_exp_handler.get_prioritized_experience(999)
# experience handler should return nothing when num_requested > size
assert states is None
states, actions, rewards, states_tp1, terminal, inds = p_exp_handler.get_prioritized_experience(2)
assert states is not None
# we should've got a terminal which would be the first element
assert np.sum(terminal) == 1
assert np.all(states_tp1[0] == np.zeros(states_tp1[0].shape))
# states should be 2 then 1
assert np.all(states[0] == 2*np.ones(states[0].shape))
assert np.all(states[1] == np.ones(states[0].shape))
# make sure we got correct actions/rewards
assert np.sum(actions) == 3 # 2+1
assert np.sum(rewards) == 3 # 2+1
# make sure all state_tp1s are correct
for ind in range(states.shape[0]):
if not terminal[ind]:
assert np.all(states[ind]+1 == states_tp1[ind])
# reinsert
for ind in inds:
p_exp_handler.tree.insert(ind, ind)
# right should be nothing because 0 has value inf
assert p_exp_handler.tree.root.right is None
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
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 train_prioritized(self, exp_handler: PrioritizedExperienceHandler, discount, cnn):
# generate minibatch data
states, actions, rewards, state_tp1s, terminal, mb_inds_popped = exp_handler.get_prioritized_experience(self.mini_batch)
if states is not None:
r_tp1 = cnn.get_output(state_tp1s)
max_tp1 = np.max(r_tp1, axis=1)
rewards += (1-terminal) * discount * max_tp1
rewVals = np.zeros((self.mini_batch, self.num_actions), dtype=self.dtype)
arange = np.arange(self.mini_batch)
rewVals[arange, actions] = rewards
mask = np.zeros((self.mini_batch, self.num_actions), dtype=self.dtype)
nonZero = np.where(rewVals != 0)
mask[nonZero[0], nonZero[1]] = 1
cost, output_states = cnn.train(states, rewVals, mask)
self.costList.append(cost)
# update prioritized exp handler with new td_error
max_states = np.max(output_states, axis=1)
td_errors = np.abs(max_tp1 - max_states)
exp_handler.set_new_td_errors(td_errors, mb_inds_popped)
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
def test_add_terminal(p_exp_handler: PrioritizedExperienceHandler):
state_size = len(p_exp_handler.states)
p_exp_handler.add_terminal()
assert p_exp_handler.tree.root.right.value == np.inf
assert p_exp_handler.tree.root.right.right.extra_vals == state_size-1