def evaluate(model_path,history_num,max_episode_steps,episode_num,result_save_path):
checkpoint = torch.load(model_path)
qnetwork = QNetwork(*checkpoint['model_hyper'])
qnetwork.load_state_dict(checkpoint['model'])
env = gym.make('MountainCar-v0')
test_success_history = []
test_reward_history = []
for episode in range(episode_num):
print('episode %d'%(episode))
observation = env.reset()
#initialize state
state = State(history_num)
state.init_state(observation)
done = False
reward_sum = 0
for t in range(max_episode_steps):
env.render()
state.display()
# select a action with max q value action
action = qnetwork.decide_action(state.toTensor().view(1,-1))
action = action.sum().item()
observation, reward, done, info = env.step(action)
reward_sum = reward_sum+reward
if done:
print('done')
print(reward_sum)
success = False
if observation[0]>=0.5:
success = True
test_success_history.append(success)
test_reward_history.append(reward_sum)
break
state.update_state_by_observation(observation,action)
print('- '*100)
print('save to %s'%(result_save_path))
with open(result_save_path,'wb') as f:
pkl.dump((test_success_history,test_reward_history),f)
done = False
final_transition = None
loss_sum = 0
reward_sum = 0
for t in range(max_episode_steps):
if done:
break
env.render()
state.display()
# select a action
p = random.random()
if p < epsilon:
action = env.action_space.sample()
else:
action = qnetwork.decide_action(state.toTensor().view(1, -1))
# to scalar and then to int
assert list(action.shape) == [1]
action = action.sum().item()
observation, reward, done, info = env.step(action)
if done:
# -1 reward for all step
avg_loss = loss_sum / max_episode_steps
print('done')
print(avg_loss)
loss_history.append(avg_loss)
reward_sum_history.append(reward_sum)
old_state = copy.deepcopy(state)
