def test(L, mouse_initial_indices, rewardlist, actions_list):
online_net = QNet(3, 4).to(device)
online_net.load_state_dict(
torch.load("./qlearning_model", map_location=device))
env = deepcopy(L)
done = False
eaubue = 0.
steps = 0
score = 0
if mouse_initial_indices is None:
all_possible_starting_positions = np.array([*np.where(L == 1)]).T
mouse_initial_indices = all_possible_starting_positions[
np.random.choice(range(len(all_possible_starting_positions)))]
state = np.array(mouse_initial_indices)
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
def progress_loop(done, steps, state, score, eaubue):
steps += 1
action = get_action(state, online_net, 1, env, True, eaubue)
displacement = np.array(actions_list[action])
newstate = state + torch.Tensor(displacement).to(device)
if env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())] != 0:
next_state = newstate
displayer.main_canva.move(displayer.mouse,
*(displacement * displayer.square_size))
reward = rewardlist[env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())]]
if env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())] == 2:
done = True
if env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist()
)] == 4: #if the mouse is in the water
env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())] = 5 #there is no more water
eaubue = 1.
else:
next_state = state
reward = rewardlist[0]
score += reward
state = next_state
print('position : ', state.tolist()[0], score)
if done is False:
displayer.window.after(
800, lambda: progress_loop(done, steps, state, score, eaubue))
displayer = Displayer()
displayer.create_labyrinth(L, mouse_initial_indices)
progress_loop(done, steps, state, score, 0.)
displayer.window.mainloop()
def main():
if not (os.path.isdir("logs")):
os.makedirs("logs")
working_dir = "logs/" + args.dir
if not (os.path.isdir(working_dir)):
raise NameError(args.dir + " does not exist in dir logs")
print(args)
env = QubeSwingupEnv(use_simulator=args.sim, batch_size= 2048*4)
num_inputs = env.observation_space.shape[0]
num_actions = NUMBER_OF_ACTIONS
print('state size:', num_inputs)
print('action size:', num_actions)
net = QNet(num_inputs, num_actions) if not args.new_net else QNet_more_layers(num_inputs, num_actions)
net.load_state_dict(torch.load(working_dir + "/best_model.pth", map_location=torch.device(device)))
net.to(device)
net.eval()
running_score = 0
epsilon = 1.0
steps = 0
beta = beta_start
loss = 0
best_running_score = -1000
for e in range(1):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
action = get_continuous_action(get_action(state, net))
if np.abs(state[0][1].item()) < deg2rad(25):
action = pd_control_policy(state.cpu().numpy()[0])[0]
next_state, reward, done, info = env.step(action)
reward = give_me_reward(info["alpha"], info["theta"])
if args.sim: env.render()
reward = give_me_reward(info["alpha"], info["theta"])
if done:
print(info)
print("theta:" , info["theta"] * 180/np.pi)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
score += reward
state = next_state
running_score = 0.99 * running_score + 0.01 * score
print('{} episode | running_score: {:.2f} | score: {:.2f} | steps: {} '.format(e, running_score, score, steps))
env.close()
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
img_shape = env.observation_space.shape
num_actions = 3
print('image size:', img_shape)
print('action size:', num_actions)
net = QNet(num_actions)
net.load_state_dict(torch.load(args.save_path + 'model.pth'))
net.to(device)
net.eval()
epsilon = 0
for e in range(5):
done = False
score = 0
state = env.reset()
state = pre_process(state)
state = torch.Tensor(state).to(device)
history = torch.stack((state, state, state, state))
for i in range(3):
action = env.action_space.sample()
state, reward, done, info = env.step(action)
state = pre_process(state)
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
history = torch.cat((state, history[:-1]), dim=0)
while not done:
if args.render:
env.render()
steps += 1
qvalue = net(history.unsqueeze(0))
action = get_action(0, qvalue, num_actions)
next_state, reward, done, info = env.step(action + 1)
next_state = pre_process(next_state)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
next_history = torch.cat((next_state, history[:-1]), dim=0)
score += reward
history = next_history
print('{} episode | score: {:.2f}'.format(e, score))
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
target_net.load_state_dict(online_net.state_dict())
online_net.share_memory()
target_net.share_memory()
optimizer = SharedAdam(online_net.parameters(), lr=lr)
global_ep, global_ep_r, res_queue = mp.Value('i',
0), mp.Value('d',
0.), mp.Queue()
writer = SummaryWriter('logs')
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
workers = [
Worker(online_net, target_net, optimizer, global_ep, global_ep_r,
res_queue, i) for i in range(mp.cpu_count())
]
[w.start() for w in workers]
res = []
while True:
r = res_queue.get()
if r is not None:
res.append(r)
[ep, ep_r, loss] = r
writer.add_scalar('log/score', float(ep_r), ep)
writer.add_scalar('log/loss', float(loss), ep)
else:
break
[w.join() for w in workers]
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
net = QNet(num_inputs, num_actions)
net.load_state_dict(torch.load(args.save_path + 'model.pth'))
net.to(device)
net.eval()
running_score = 0
steps = 0
for e in range(5):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
env.render()
steps += 1
qvalue = net(state)
action = get_action(qvalue)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
score += reward
state = next_state
print('{} episode | score: {:.2f}'.format(e, score))
def main():
net = QNet().cuda().train()
# print(net)
optimizer = optim.SGD([{
'params': [
param
for name, param in net.named_parameters() if name[-4:] == 'bias'
],
'lr':
2 * args['lr']
}, {
'params': [
param
for name, param in net.named_parameters() if name[-4:] != 'bias'
],
'lr':
args['lr'],
'weight_decay':
args['weight_decay']
}],
momentum=args['momentum'])
if len(args['snapshot']) > 0:
print('training resumes from ' + args['snapshot'])
net.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name, args['snapshot'] + '.pth')))
optimizer.load_state_dict(
torch.load(
os.path.join(ckpt_path, exp_name,
args['snapshot'] + '_optim.pth')))
optimizer.param_groups[0]['lr'] = 2 * args['lr']
optimizer.param_groups[1]['lr'] = args['lr']
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
open(log_path, 'w').write(str(args) + '\n\n')
train(net, optimizer)
def __init__(self, run):
self.run = run
ckpt_dir = os.path.join(run, 'ckpt')
ckpts = glob2.glob(os.path.join(ckpt_dir, '*.pth'))
assert ckpts, "No checkpoints to resume from!"
def get_epoch(ckpt_url):
s = re.findall("ckpt_e(\d+).pth", ckpt_url)
epoch = int(s[0]) if s else -1
return epoch, ckpt_url
start_epoch, ckpt = max(get_epoch(c) for c in ckpts)
print('Checkpoint:', ckpt)
if torch.cuda.is_available():
model = QNet().cuda()
else:
model = QNet()
ckpt = torch.load(ckpt)
model.load_state_dict(ckpt['model'])
model.eval()
self.model = model
class Learner:
def __init__(self, n_actors, device='cuda:0'):
# params
self.gamma = 0.99
self.alpha = 0.6
self.bootstrap_steps = 3
self.initial_exploration = 50000
self.priority_epsilon = 1e-6
self.device = device
self.n_epochs = 0
self.n_actors = n_actors
# path
self.memory_path = os.path.join('./', 'logs', 'memory')
self.net_path = os.path.join('./', 'logs', 'model', 'net.pt')
self.target_net_path = os.path.join('./', 'logs', 'model',
'target_net.pt')
# memory
self.memory_size = 500000
self.batch_size = 128
self.memory_load_interval = 10
self.replay_memory = ReplayMemory(self.memory_size, self.batch_size,
self.bootstrap_steps)
# net
self.net_save_interval = 50
self.target_update_interval = 1000
self.net = QNet(self.net_path, self.device).to(self.device)
self.target_net = QNet(self.target_net_path,
self.device).to(self.device)
self.target_net.load_state_dict(self.net.state_dict())
self.net.save()
self.target_net.save()
self.optim = optim.RMSprop(self.net.parameters(),
lr=0.00025 / 4.0,
alpha=0.95,
eps=1.5e-7,
centered=True)
def run(self):
while True:
if self.replay_memory.size > self.initial_exploration:
self.train()
self.interval()
def train(self):
batch, index, weights = self.replay_memory.sample(self.device)
# q_value
q_value = self.net(batch['state'])
q_value = q_value.gather(1, batch['action'])
# target q_value
with torch.no_grad():
next_action = torch.argmax(self.net(batch["next_state"]),
1).view(-1, 1)
next_q_value = self.target_net(batch["next_state"]).gather(
1, next_action)
target_q_value = batch["reward"] + (
self.gamma**
self.bootstrap_steps) * next_q_value * (1 - batch['done'])
# update
self.optim.zero_grad()
loss = torch.mean(0.5 * (q_value - target_q_value)**2)
loss.backward()
self.optim.step()
priority = (np.abs(
(q_value - target_q_value).detach().cpu().numpy()).reshape(-1) +
self.priority_epsilon)**self.alpha
self.replay_memory.update_priority(index, priority)
def interval(self):
self.n_epochs += 1
if self.n_epochs % self.target_update_interval == 0:
self.target_net.load_state_dict(self.net.state_dict())
if self.n_epochs % self.net_save_interval == 0:
self.net.save()
self.target_net.save()
if self.n_epochs % self.memory_load_interval == 0:
for i in range(self.n_actors):
self.replay_memory.load(self.memory_path, i)
class Actor:
def __init__(self, actor_id, n_actors, shared_dict, device='cpu'):
# params
self.gamma = 0.99
self.epsilon = 0.4 ** (1 + actor_id * 7 / (n_actors - 1))
self.bootstrap_steps = 3
self.alpha = 0.6
self.priority_epsilon = 1e-6
self.device = device
self.actor_id = actor_id
# path
self.memory_path = os.path.join(
'./', 'logs', 'memory')
# memory
self.memory_size = 50000
self.batch_size = 32
self.action_repeat = 4
self.n_stacks = 4
self.burn_in_length = 10
self.learning_length = 10
self.overlap_length = 10
self.eta = 0.9
self.sequence_length = self.burn_in_length + self.learning_length
self.stack_count = self.n_stacks // self.action_repeat
self.memory_save_interval = 5
self.episode_start_index = 0
self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma)
self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps)
# net
self.shared_dict = shared_dict
self.net_load_interval = 5
self.net = QNet(self.device).to(self.device)
self.target_net = QNet(self.device).to(self.device)
self.target_net.load_state_dict(self.net.state_dict())
# env
self.env = PongEnv(self.action_repeat, self.n_stacks)
self.episode_reward = 0
self.n_episodes = 0
self.n_steps = 0
self.memory_count = 0
self.state = self.env.reset()
def run(self):
while True:
self.step()
def step(self):
state = self.state
action, q_value, h, c, target_q_value, target_h, target_c = self.select_action(state)
q_value = q_value.detach().cpu().numpy()
target_q_value = target_q_value.detach().cpu().numpy()
next_state, reward, done, _ = self.env.step(action)
self.episode_reward += reward
self.n_steps += 1
self.n_steps_memory.add(q_value, state[-self.action_repeat:], h, c, target_h, target_c, action, reward, self.stack_count)
if self.stack_count > 1:
self.stack_count -= 1
if self.n_steps > self.bootstrap_steps:
pre_q_value, state, h, c, target_h, target_c, action, reward, stack_count = self.n_steps_memory.get()
priority = self.calc_priority(pre_q_value, action, reward, q_value, target_q_value, done)
self.replay_memory.add(state, h, c, target_h, target_c, action, reward, done, stack_count, priority)
self.memory_count += 1
self.state = next_state.copy()
if done:
while self.n_steps_memory.size > 0:
pre_q_value, state, h, c, target_h, target_c, action, reward, stack_count = self.n_steps_memory.get()
priority = self.calc_priority(pre_q_value, action, reward, q_value, target_q_value, done)
self.replay_memory.add(state, h, c, target_h, target_c, action, reward, done, stack_count, priority)
self.memory_count += 1
self.reset()
def select_action(self, state):
state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
with torch.no_grad():
q_value, h, c = self.net(state, True)
target_q_value, target_h, target_c = self.target_net(state, True)
if np.random.random() < self.epsilon:
action = np.random.randint(6)
else:
action = q_value.argmax().item()
return action, q_value, h, c, target_q_value, target_h, target_c
def reset(self):
if self.n_episodes % 1 == 0:
print('episodes:', self.n_episodes, 'actor_id:', self.actor_id, 'return:', self.episode_reward)
self.net.reset()
self.target_net.reset()
self.set_seq_start_index()
self.state = self.env.reset()
self.episode_start_index = self.replay_memory.index
self.episode_reward = 0
self.n_episodes += 1
self.n_steps = 0
self.memory_count = 0
self.stack_count = self.n_stacks // self.action_repeat
# reset n_step memory
self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma)
# save replay memory
if self.n_episodes % self.memory_save_interval == 0:
self.replay_memory.save(self.memory_path, self.actor_id)
self.replay_memory = ReplayMemory(self.memory_size, self.batch_size, self.bootstrap_steps)
self.episode_start_index = 0
gc.collect()
# load net
if self.n_episodes % self.net_load_interval == 0:
self.load_model()
def load_model(self):
try:
self.net.load_state_dict(self.shared_dict['net_state'])
self.target_net.load_state_dict(self.shared_dict['target_net_state'])
except:
print('load error')
def calc_priority(self, q_value, action, reward, next_q_value, target_next_q_value, done):
q_value = q_value.reshape(-1)[action]
target_next_q_value = target_next_q_value.reshape(-1)
if done:
target_q_value = reward
else:
next_action = next_q_value.argmax(-1)
target_next_q_value = target_next_q_value[next_action]
target_q_value = reward + (self.gamma**self.bootstrap_steps) * target_next_q_value
priority = np.abs(q_value - target_q_value) + self.priority_epsilon
priority = priority ** self.alpha
return priority
def set_seq_start_index(self):
last_index = self.replay_memory.index
start_index = self.episode_start_index
seq_start_index = [i for i in range(start_index, last_index-self.sequence_length, self.overlap_length)]
seq_start_index.append(last_index - self.sequence_length)
seq_start_index = np.array(seq_start_index)
self.replay_memory.update_sequence_priority(seq_start_index)
self.replay_memory.memory['is_seq_start'][seq_start_index] = 1
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
print('state size:', state_size)
print('action size:', action_size)
q_net = QNet(state_size, action_size, args)
if args.load_model is not None:
pretrained_model_path = os.path.join(os.getcwd(), 'save_model',
str(args.load_model))
pretrained_model = torch.load(pretrained_model_path)
q_net.load_state_dict(pretrained_model)
steps = 0
for episode in range(args.iter):
done = False
score = 0
state = env.reset()
state = np.reshape(state, [1, state_size])
while not done:
if args.render:
env.render()
steps += 1
class QTDAgent(object):
def __init__(self,
state_dim,
action_dim,
learning_rate=0.001,
reward_decay=0.99,
e_greedy=0.9):
self.action_dim = action_dim
self.state_dim = state_dim
self.lr = learning_rate
self.gamma = reward_decay # in according to the parameters in the formulation.
self.epsilon = e_greedy
self.EPS_START = 0.9
self.EPS_END = 0.05
self.EPS_DECAY = 30000 # this decay is to slow. # TO DO: figure out the relationship between the decay and the totoal step.
# try to use a good strategy to solve this problem.
use_cuda = torch.cuda.is_available()
self.LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
self.FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
self.model = QNet(self.state_dim,
self.action_dim).cuda() if use_cuda else QNet(
self.state_dim, self.action_dim)
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
# self.scheduler = optim.StepLR(self.optimizer, step_size=10000, gamma=0.5) # the learning rate decrease by a factor gamma every 10000 step_size.
util.weights_init(self.model)
def sbc(self, v, volatile=False):
return Variable(self.FloatTensor((np.expand_dims(v, 0).tolist())),
volatile=volatile)
def get_actions(self, state):
action = self.model(self.sbc(state, volatile=True))
return action
def select_action(self, state, steps_done):
util.adjust_learning_rate(self.optimizer,
self.lr,
steps_done,
10000,
lr_decay=0.2) # global steps_done
sample = random.random()
esp_threshold = self.EPS_END + (self.EPS_START - self.EPS_END) * \
np.exp(-1. * steps_done / self.EPS_DECAY)
if sample > esp_threshold:
actions = self.get_actions(state)
action = actions.data.max(1)[1].view(1, 1)
return action
else:
return self.LongTensor([[random.randrange(self.action_dim)]])
def update(self, pending): # def update(self, s, a, r, s_, a_,done=False):
pending_len = len(pending)
loss = 0
while (pending_len):
pending_len = pending_len - 1
[s, a, r, s_, a_, done] = pending[pending_len]
if (done == True):
expect_state_action_value = r
else:
non_final_next_states = self.model(self.sbc(s_, volatile=True))
expect_state_action_value = r + self.gamma * non_final_next_states.max(
1)[0]
expect_state_action_value.volatile = False
# expect_state_action_value = r + self.gamma*self.model(Variable(torch.from_numpy(np.expand_dims(s_,0).astype('float32')))).max(1)[0]
state_action_value = self.model(self.sbc(s))[0, a]
loss += 0.5 * (state_action_value -
expect_state_action_value).pow(2)
self.optimizer.zero_grad()
loss.backward()
# loss.backward()
# for param in self.model.parameters():
# param.grad.data.clamp_(-1,1)
self.optimizer.step()
def save_model(self, path):
torch.save(self.model.state_dict(), '{}QTDAgent.pt'.format(path))
# torch.save(self.target_critic.state_dict(), '{}/critic.pt'.format(path))
print('Models saved successfully')
def load_model(self, name):
self.model.load_state_dict(name)
def train(render):
online_net = QNet(h=84, w=84, outputs=36)
online_net.load_state_dict(torch.load('saved/online_net.pt'))
target_net = QNet(h=84, w=84, outputs=36)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory(replay_memory_capacity)
memory = torch.load('saved/model_memory.pt')
epsilon = 0.1
steps = 0
beta = beta_start
loss = 0
for e in range(100000):
#level = random.choice(LEVEL_SET)
level = 'Level01'
env = make_retro(game=env_name,
state=level,
use_restricted_actions=retro.Actions.DISCRETE)
done = False
total_reward = 0.0
state = env.reset()
state = torch.Tensor(state).to(device).permute(2, 0, 1)
#state = state.view(state.size()[0], -1)
state = state.unsqueeze(0)
while not done:
steps += 1
action = get_action(state.to(device), target_net, epsilon, env)
if render:
env.render()
next_state, reward, done, info = env.step(action)
next_state = torch.Tensor(next_state).permute(2, 0, 1)
#next_state = next_state.view(next_state.size()[0], -1)
next_state = next_state.unsqueeze(0)
total_reward += reward
mask = 0 if done else 1
action_one_hot = torch.zeros(36)
action_one_hot[action] = 1
reward = torch.tensor([info['score']]).to(device)
memory.push(state, next_state, action_one_hot, reward, mask)
state = next_state
if len(memory) > initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.02)
beta += 0.00005
beta = min(1, beta)
batch, weights = memory.sample(batch_size, online_net,
target_net, beta)
loss = QNet.train_model(online_net, target_net, optimizer,
batch, weights)
if steps % update_target == 0:
update_target_model(online_net, target_net)
if e % 1 == 0:
print('{} episode | Total Reward: {}'.format(e, total_reward))
torch.save(online_net.state_dict(), 'saved/online_net.pt')
torch.save(memory, 'saved/model_memory.pt')
env.close()
class Actor:
def __init__(self, actor_id, n_actors, device='cpu'):
# params
self.gamma = 0.99
self.epsilon = 0.4**(1 + actor_id * 7 / (n_actors - 1))
self.bootstrap_steps = 3
self.alpha = 0.6
self.priority_epsilon = 1e-6
self.device = device
self.actor_id = actor_id
# path
self.memory_path = os.path.join('./', 'logs', 'memory')
self.net_path = os.path.join('./', 'logs', 'model', 'net.pt')
self.target_net_path = os.path.join('./', 'logs', 'model',
'target_net.pt')
# memory
self.memory_size = 50000
self.batch_size = 32
self.action_repeat = 4
self.n_stacks = 4
self.stack_count = self.n_stacks // self.action_repeat
self.memory_save_interval = 1
self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma)
self.replay_memory = ReplayMemory(self.memory_size, self.batch_size,
self.bootstrap_steps)
# net
self.net_load_interval = 5
self.net = QNet(self.net_path).to(self.device)
self.target_net = QNet(self.target_net_path).to(self.device)
self.target_net.load_state_dict(self.net.state_dict())
# env
self.env = PongEnv(self.action_repeat, self.n_stacks)
self.episode_reward = 0
self.n_episodes = 0
self.n_steps = 0
self.memory_count = 0
self.state = self.env.reset()
def run(self):
while True:
self.step()
def step(self):
state = self.state
action = self.select_action(state)
next_state, reward, done, _ = self.env.step(action)
self.episode_reward += reward
self.n_steps += 1
self.n_steps_memory.add(state[-self.action_repeat:], action, reward,
self.stack_count)
if self.stack_count > 1:
self.stack_count -= 1
if self.n_steps > self.bootstrap_steps:
state, action, reward, stack_count = self.n_steps_memory.get()
self.replay_memory.add(state, action, reward, done, stack_count)
self.memory_count += 1
self.state = next_state.copy()
if done:
while self.n_steps_memory.size > 0:
state, action, reward, stack_count = self.n_steps_memory.get()
self.replay_memory.add(state, action, reward, done,
stack_count)
self.memory_count += 1
self.reset()
def select_action(self, state):
if np.random.random() < self.epsilon:
action = np.random.randint(6)
else:
state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
with torch.no_grad():
q_val = self.net(state)
action = q_val.argmax().item()
return action
def reset(self):
if self.n_episodes % 1 == 0:
print('episodes:', self.n_episodes, 'actor_id:', self.actor_id,
'return:', self.episode_reward)
self.calc_priority()
self.state = self.env.reset()
self.episode_reward = 0
self.n_episodes += 1
self.n_steps = 0
self.memory_count = 0
self.stack_count = self.n_stacks // self.action_repeat
# reset n_step memory
self.n_steps_memory = NStepMemory(self.bootstrap_steps, self.gamma)
# save replay memory
if self.n_episodes % self.memory_save_interval == 0:
self.replay_memory.save(self.memory_path, self.actor_id)
self.replay_memory = ReplayMemory(self.memory_size,
self.batch_size,
self.bootstrap_steps)
# load net
if self.n_episodes % self.net_load_interval == 0:
self.net.load()
self.target_net.load()
def calc_priority(self):
last_index = self.replay_memory.size
start_index = last_index - self.memory_count
batch, index = self.replay_memory.indexing_sample(
start_index, last_index, self.device)
batch_size = batch['state'].shape[0]
priority = np.zeros(batch_size, dtype=np.float32)
mini_batch_size = 500
for start_index in range(0, batch_size, mini_batch_size):
last_index = min(start_index + mini_batch_size, batch_size)
mini_batch = dict()
for key in batch.keys():
if key in ['reward', 'done']:
mini_batch[key] = batch[key][start_index:last_index]
else:
mini_batch[key] = torch.tensor(
batch[key][start_index:last_index]).to(self.device)
mini_batch['action'] = mini_batch['action'].view(-1, 1).long()
with torch.no_grad():
# q_value
q_value = self.net(mini_batch['state']).gather(
1, mini_batch['action']).view(-1, 1).cpu().numpy()
# taget_q_value
next_action = torch.argmax(self.net(mini_batch['next_state']),
1).view(-1, 1)
next_q_value = self.target_net(
mini_batch['next_state']).gather(
1, next_action).cpu().numpy()
target_q_value = mini_batch['reward'] + (
self.gamma**
self.bootstrap_steps) * next_q_value * (1 - mini_batch['done'])
delta = np.abs(q_value -
target_q_value).reshape(-1) + self.priority_epsilon
delta = delta**self.alpha
priority[start_index:last_index] = delta
self.replay_memory.update_priority(index, priority)
def test(level_list, render=True):
online_net = QNet(h=84, w=84, outputs=36)
online_net.load_state_dict(torch.load('saved/online_net.pt'))
online_net.to(device)
cnt = 0
death = 0
total_reward = 0.0
str_level_list = [LEVEL_SET[idx - 1] for idx in level_list]
for level in str_level_list:
env = make_retro(game=env_name,
state=level,
use_restricted_actions=retro.Actions.DISCRETE)
obs = env.reset()
state = torch.Tensor(obs).to(device).permute(2, 0, 1)
#state = state.view(state.size()[0], -1)
state = state.unsqueeze(0)
previous_lives = 3
previous_level = level_list[cnt]
cnt += 1
if death >= 3:
break
for t in count():
action = online_net.get_action(state.to(device))
if render:
env.render()
time.sleep(0.02)
next_state, reward, done, info = env.step(action)
next_state = torch.Tensor(next_state).permute(2, 0, 1)
#next_state = next_state.view(next_state.size()[0], -1)
next_state = next_state.unsqueeze(0)
total_reward += reward
current_lives = info['lives']
current_level = info['level']
if current_lives != previous_lives:
print('Dead')
previous_lives = info['lives']
death += 1
#if death >= 3:
# print("Finished ", level, " Total reward: {}".format(total_reward))
# break
if current_level != previous_level:
print('Stage changed')
print("Finished ", level,
" Total reward: {}".format(total_reward))
break
state = next_state
if done:
print('All lives gone')
print("Finished ", level,
" Total reward: {}".format(total_reward))
break
env.close()
return
optimizer.step()
return loss
# Build environment
env = make_atari('PongNoFrameskip-v4', stack=2)
env = wrap_pytorch(env)
number_actions = env.action_space.n
replay_buffer = ReplayBuffer(replay_memory_size)
# Separate target net & policy net
input_shape = env.reset().shape
current_net = QNet(input_shape, number_actions).to(device)
target_net = QNet(input_shape, number_actions).to(device) # with older weights
target_net.load_state_dict(current_net.state_dict())
target_net.eval()
optimizer = opt_algorithm(current_net.parameters(), lr=learning_rate)
n_episode = 1
episode_return = 0
best_return = 0
returns = []
state = env.reset()
for i in count():
# env.render()
eps = get_epsilon(i)
action = select_action(state,
current_net,
eps,
number_action=number_actions)
start_epoch = 1
if args.resume:
ckpt_dir_r = os.path.join(args.resume, 'ckpt')
ckpts = glob2.glob(os.path.join(ckpt_dir_r, '*.pth'))
assert ckpts, "No checkpoints to resume from!"
def get_epoch(ckpt_url):
s = re.findall("ckpt_e(\d+).pth", ckpt_url)
epoch = int(s[0]) if s else -1
return epoch, ckpt_url
start_epoch, ckpt = max(get_epoch(c) for c in ckpts)
print('Checkpoint:', ckpt)
ckpt = torch.load(ckpt)
model.load_state_dict(ckpt['model'])
start_epoch = ckpt['epoch']
best_mse = ckpt['mse_val']
start_epoch = ckpt['epoch']
for epoch in trange(start_epoch, args.epochs + 1):
cur_loss = trainEval(train_loader, model, optimizer, args, True)
val_loss = trainEval(val_loader, model, optimizer, args, False)
test_loss = trainEval(test_loader, model, optimizer, args, False)
metrics = {'epoch': epoch}
metrics['mse_train'] = cur_loss
metrics['mse_val'] = val_loss
metrics['mse_test'] = test_loss
log = log.append(metrics, ignore_index=True)
log.to_csv(log_file, index=False)
class Learner:
def __init__(self, n_actors, shared_dict, device='cuda:0'):
# params
self.gamma = 0.99
self.alpha = 0.6
self.bootstrap_steps = 3
self.initial_exploration = 50000
self.priority_epsilon = 1e-6
self.device = device
self.n_epochs = 0
self.n_actors = n_actors
# path
self.memory_path = os.path.join('./', 'logs', 'memory')
# memory
self.burn_in_length = 10
self.learning_length = 10
self.sequence_length = self.burn_in_length + self.learning_length
self.memory_size = 500000
self.batch_size = 8
self.memory_load_interval = 20
self.replay_memory = ReplayMemory(self.memory_size, self.batch_size,
self.bootstrap_steps)
# net
self.shared_dict = shared_dict
self.net_save_interval = 100
self.target_update_interval = 1000
self.net = QNet(self.device).to(self.device)
self.target_net = QNet(self.device).to(self.device)
self.target_net.load_state_dict(self.net.state_dict())
self.save_model()
self.optim = optim.RMSprop(self.net.parameters(),
lr=0.00025 / 4.0,
alpha=0.95,
eps=1.5e-7,
centered=True)
def run(self):
while True:
if self.replay_memory.size > self.initial_exploration:
self.train()
if self.n_epochs % 100 == 0:
print('trained', self.n_epochs, 'epochs')
self.interval()
def train(self):
batch, seq_index, index = self.replay_memory.sample(self.device)
self.net.set_state(batch['hs'], batch['cs'])
self.target_net.set_state(batch['target_hs'], batch['target_cs'])
### burn-in step ###
state = batch['state'][:self.burn_in_length]
next_state = batch['next_state'][:self.burn_in_length]
with torch.no_grad():
_ = self.net(state)
_ = self.target_net(next_state)
### learning step ###
state = batch['state'][self.burn_in_length:]
next_state = batch['next_state'][self.burn_in_length:]
# q_value
q_value = self.net(state).gather(1, batch['action'].view(-1, 1))
# target q_value
with torch.no_grad():
next_action = torch.argmax(self.net(next_state), 1).view(-1, 1)
next_q_value = self.target_net(next_state).gather(1, next_action)
target_q_value = batch["reward"].view(
-1, 1) + (self.gamma**self.bootstrap_steps) * next_q_value * (
1 - batch['done'].view(-1, 1))
# update
self.optim.zero_grad()
loss = torch.mean(0.5 * (q_value - target_q_value)**2)
loss.backward()
self.optim.step()
priority = (np.abs(
(q_value - target_q_value).detach().cpu().numpy()).reshape(-1) +
self.priority_epsilon)**self.alpha
self.replay_memory.update_priority(
index[self.burn_in_length:].reshape(-1), priority)
self.replay_memory.update_sequence_priority(seq_index, True)
def interval(self):
self.n_epochs += 1
if self.n_epochs % self.target_update_interval == 0:
self.target_net.load_state_dict(self.net.state_dict())
if self.n_epochs % self.net_save_interval == 0:
self.save_model()
if self.n_epochs % self.memory_load_interval == 0:
for i in range(self.n_actors):
self.replay_memory.load(self.memory_path, i)
def save_model(self):
self.shared_dict['net_state'] = deepcopy(self.net).cpu().state_dict()
self.shared_dict['target_net_state'] = deepcopy(
self.target_net).cpu().state_dict()
else:
input = torch.from_numpy(state).to(device, torch.float32).unsqueeze(0)
score = net(input)
action = score.max(dim=1)[1].to(torch.int64).item()
return action
# Build environment
env = make_atari('PongNoFrameskip-v4', stack=2)
env = wrap_pytorch(env)
env = gym.wrappers.Monitor(env,
directory='./movie',
force=True,
video_callable=lambda x: True)
number_actions = env.action_space.n
# Separate target net & policy net
input_shape = env.reset().shape
net = QNet(input_shape, number_actions)
net.load_state_dict(torch.load(model))
net.eval().to(device)
for episode in range(10):
state = env.reset()
done = False
while not done:
# env.render()
action = select_action(state, number_actions=number_actions)
next_state, reward, done, _ = env.step(action)
state = next_state
env.close()
