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