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 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)
