window.append(next_state)
next_phi = window()
else:
atlas.clear()
next_phi = None
else:
next_phi = None
# Store the transition in memory if valid.
# Torch the reward.
if phi is not None and next_phi is not None:
if isinstance(reward, float) and isinstance(action, float):
tensor_reward = torch.tensor([reward])
tensor_action = torch.tensor([action])
memory.push(phi, tensor_action, next_phi, tensor_reward)
# Move to the next state.
phi = next_phi
# dont update if this is validation
#if (i_episode + 1) % 5 == 0:
# continue
# perform the optimization.
if len(memory) > 128:
trainer.optimize()
# Check the episode counter to end simulation.
if counter > MAX_EPISODE_COUNTER:
done = True
print('Action threshold met', frames)
action = select_action(state)
_, reward, done, info = env.step(action)
reward = torch.tensor([reward], device=device)
# Observe new state
last_screen = current_screen
current_screen = get_screen()
if not done:
next_state = current_screen - last_screen
else:
next_state = None
# Store the transition in memory
memory.push(state, action, next_state, reward)
# Move to the next state
state = next_state
optimize_model()
# print(len(memory))
# Break if pacman is caught.
if done:
break
# print(done) # bool
# print(info) # json with lives
# time.sleep(0.1)
class DQNAgent(GymAgent):
"""
an agent for running the DQN algorithm (Minh et al 2013)
"""
def __init__(self, env, mode, pre_trained_model, tensorboard_writer=None):
super(DQNAgent, self).__init__(env, mode, tensorboard_writer)
self.agent_name = 'DQN' + str(self.agent_no)
self.memory = ReplayMemory()
self.network = DeepQNetwork(self.obs_space[0], self.action_space)
if self.mode == 'play':
self.network.load_params(pre_trained_model)
self.network.eval()
elif self.mode == 'train':
self.eval_network = DeepQNetwork(self.obs_space[0],
self.action_space)
self.eval_network.eval()
if pre_trained_model:
self.eval_network.load_params(pre_trained_model)
self.optimizer = optim.RMSprop(self.network.parameters(), lr=LR)
self.loss_func = SmoothL1Loss()
else:
raise ValueError(
'Please set a valid mode for the agent (play or train)')
def interact(self, state, action):
"""
returns:
state, reward, done, info
"""
return self.env.step(action, state)
def select_action(self, state):
if self.mode == 'play':
return self.network(prep_exploitation(state)).max(1)[1].view(1, 1)
##epsilon greedy policy
eps_threshold = EPS_START * EPS_DECAY**self.no_training_steps if EPS_DECAY > EPS_END else EPS_END
self.no_training_steps += 1
if random.random() > eps_threshold:
with torch.no_grad():
return self.network(prep_exploitation(state)).max(1)[1].view(
1, 1)
else:
return prep_exploration(self.action_space)
def optimize(self):
sum_loss = 0
if len(self.memory) < BATCH_SIZE:
batch_size = len(self.memory)
else:
batch_size = BATCH_SIZE
s, a, _s, r = prep_mem_batch(self.memory.sample(batch_size))
non_final_next = torch.cat([sa for sa in _s if sa is not None])
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, _s)))
state_action_values = self.network(s).gather(1, a.long().unsqueeze(1))
next_state_values = torch.zeros(batch_size)
next_state_values[non_final_mask] = self.eval_network(
non_final_next).detach().max(1)[0]
expected_q = prep_q(next_state_values, r)
loss = self.loss_func(state_action_values, expected_q.unsqueeze(1))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.item()
def train(self, num_episodes, render=False, lr_decay=False):
end_state = np.zeros(self.obs_space)
state = end_state
for episode in range(1, num_episodes + 1):
done = False
timesteps = 0
rewards = []
sum_rewards = []
loss = 0
times_alive = []
while not done:
if state is end_state:
state = self.env.initialize()
if render: self.env.render()
action = self.select_action(state)
_state, reward, done, _ = self.interact(action.item(), state)
rewards.append(reward)
timesteps += 1
if done:
_state = end_state
sum_reward = np.sum(rewards)
sum_rewards.append(sum_reward)
mean_loss = loss / timesteps
times_alive.append(timesteps)
timesteps = 0
if self.writer:
self.writer.add_scalar(
self.agent_name + 'duration of episode', timesteps,
episode)
self.writer.add_scalar(
self.agent_name + 'mean reward of episode',
sum_reward, episode)
self.writer.add_scalar(
self.agent_name + 'mean loss of episode',
mean_loss, episode)
self.memory.push(state, action,
_state if _state is not None else end_state,
reward)
state = _state
episode_loss = self.optimize()
loss += episode_loss
if lr_decay:
for g in self.optimizer.param_groups:
g['lr'] = g['lr'] / (1 + (episode / LR_DECAY))
if episode % TARGET_UPDATE == 0:
if self.env.goal(times_alive):
print('goal reached your computer is smart :)')
self.eval_network.save_params(self.agent_name,
self.env.env_name)
break
else:
times_alive = []
self.eval_network.update_params(self.network)
print('episode ', episode, 'loss ', mean_loss, 'reward ',
np.mean(sum_rewards))
#add your custom goals
def play(self, num_episodes):
for episode in range(1, num_episodes + 1):
done = False
state = self.env.initialize()
while not done:
self.env.render()
action = self.select_action(state)
_state, reward, done, _ = self.interact(action.item(), state)
if done:
state = self.env.initialize()