if __name__ == "__main__":
# maze game
env = Env()
RL = DeepQNetwork(env.n_actions,
env.n_features,
learning_rate=0.0001,
reward_decay=0.9,
e_greedy=0.75,
replace_target_iter=2000,
memory_size=MEMORYCAPACITY,
batch_size=64
# output_graph=True
)
RL.restore_model()
for episode in range(EPS):
env.build_map()
value = 0
for step in range(STEP):
state = env.state.copy()
action = RL.choose_action(state)
env.step(action_space[action])
state_ = env.state.copy()
reward, dist = compute_reward(state, state_)
RL.store_transition(state, action, reward, state_)
value += reward
if dist < DIST:
break
if RL.memory_counter > MEMORYCAPACITY:
