class Agent:
def __init__(
self,
env: 'Environment',
input_frame: ('int: the number of channels of input image'),
input_dim: (
'int: the width and height of pre-processed input image'),
num_frames: ('int: Total number of frames'),
eps_decay: ('float: Epsilon Decay_rate'),
gamma: ('float: Discount Factor'),
target_update_freq: ('int: Target Update Frequency (by frames)'),
update_type: (
'str: Update type for target network. Hard or Soft') = 'hard',
soft_update_tau: ('float: Soft update ratio') = None,
batch_size: ('int: Update batch size') = 32,
buffer_size: ('int: Replay buffer size') = 1000000,
update_start_buffer_size: (
'int: Update starting buffer size') = 50000,
learning_rate: ('float: Learning rate') = 0.0004,
eps_min: ('float: Epsilon Min') = 0.1,
eps_max: ('float: Epsilon Max') = 1.0,
device_num: ('int: GPU device number') = 0,
rand_seed: ('int: Random seed') = None,
plot_option: ('str: Plotting option') = False,
model_path: ('str: Model saving path') = './'):
self.action_dim = env.action_space.n
self.device = torch.device(
f'cuda:{device_num}' if torch.cuda.is_available() else 'cpu')
self.model_path = model_path
self.env = env
self.input_frames = input_frame
self.input_dim = input_dim
self.num_frames = num_frames
self.epsilon = eps_max
self.eps_decay = eps_decay
self.eps_min = eps_min
self.gamma = gamma
self.target_update_freq = target_update_freq
self.update_cnt = 0
self.update_type = update_type
self.tau = soft_update_tau
self.batch_size = batch_size
self.buffer_size = buffer_size
self.update_start = update_start_buffer_size
self.seed = rand_seed
self.plot_option = plot_option
self.q_current = QNetwork(
(self.input_frames, self.input_dim, self.input_dim),
self.action_dim).to(self.device)
self.q_target = QNetwork(
(self.input_frames, self.input_dim, self.input_dim),
self.action_dim).to(self.device)
self.q_target.load_state_dict(self.q_current.state_dict())
self.q_target.eval()
self.optimizer = optim.Adam(self.q_current.parameters(),
lr=learning_rate)
self.memory = ReplayBuffer(
self.buffer_size,
(self.input_frames, self.input_dim, self.input_dim),
self.batch_size)
def select_action(
self, state:
'Must be pre-processed in the same way while updating current Q network. See def _compute_loss'
):
if np.random.random() < self.epsilon:
return np.zeros(self.action_dim), self.env.action_space.sample()
else:
# if normalization is applied to the image such as devision by 255, MUST be expressed 'state/255' below.
state = torch.FloatTensor(state).to(self.device).unsqueeze(0) / 255
Qs = self.q_current(state)
action = Qs.argmax()
return Qs.detach().cpu().numpy(), action.detach().item()
def processing_resize_and_gray(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) # Pure
# frame = cv2.cvtColor(frame[:177, 32:128, :], cv2.COLOR_RGB2GRAY) # Boxing
# frame = cv2.cvtColor(frame[2:198, 7:-7, :], cv2.COLOR_RGB2GRAY) # Breakout
frame = cv2.resize(frame,
dsize=(self.input_dim, self.input_dim)).reshape(
self.input_dim, self.input_dim).astype(np.uint8)
return frame
def get_state(self, action, skipped_frame=0):
'''
num_frames: how many frames to be merged
input_size: hight and width of input resized image
skipped_frame: how many frames to be skipped
'''
next_state = np.zeros(
(self.input_frames, self.input_dim, self.input_dim))
rewards = 0
dones = 0
for i in range(self.input_frames):
for j in range(skipped_frame):
state, reward, done, _ = self.env.step(action)
rewards += reward
dones += int(done)
state, reward, done, _ = self.env.step(action)
next_state[i] = self.processing_resize_and_gray(state)
rewards += reward
dones += int(done)
return rewards, next_state, dones
def get_init_state(self):
state = self.env.reset()
action = self.env.action_space.sample()
_, state, _ = self.get_state(action, skipped_frame=0)
return state
def store(self, state, action, reward, next_state, done):
self.memory.store(state, action, reward, next_state, done)
def update_current_q_net(self):
batch = self.memory.batch_load()
loss = self._compute_loss(batch)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.item()
def target_soft_update(self):
for target_param, current_param in zip(self.q_target.parameters(),
self.q_current.parameters()):
target_param.data.copy_(self.tau * current_param.data +
(1.0 - self.tau) * target_param.data)
def target_hard_update(self):
self.update_cnt = (self.update_cnt + 1) % self.target_update_freq
if self.update_cnt == 0:
self.q_target.load_state_dict(self.q_current.state_dict())
def train(self):
tic = time.time()
losses = []
scores = []
epsilons = []
avg_scores = [[-1000]]
score = 0
print("Storing initial buffer..")
state = self.get_init_state()
for frame_idx in range(1, self.update_start + 1):
_, action = self.select_action(state)
reward, next_state, done = self.get_state(action, skipped_frame=0)
self.store(state, action, reward, next_state, done)
state = next_state
if done: state = self.get_init_state()
print("Done. Start learning..")
history_store = []
for frame_idx in range(1, self.num_frames + 1):
Qs, action = self.select_action(state)
reward, next_state, done = self.get_state(action, skipped_frame=0)
self.store(state, action, reward, next_state, done)
history_store.append([state, Qs, action, reward, next_state, done])
loss = self.update_current_q_net()
if self.update_type == 'hard': self.target_hard_update()
elif self.update_type == 'soft': self.target_soft_update()
score += reward
losses.append(loss)
if done:
scores.append(score)
if np.mean(scores[-10:]) > max(avg_scores):
torch.save(
self.q_current.state_dict(),
self.model_path + '{}_Score:{}.pt'.format(
frame_idx, np.mean(scores[-10:])))
training_time = round((time.time() - tic) / 3600, 1)
np.save(
self.model_path +
'{}_history_Score_{}_{}hrs.npy'.format(
frame_idx, score, training_time),
np.array(history_store))
print(
" | Model saved. Recent scores: {}, Training time: {}hrs"
.format(scores[-10:], training_time),
' /'.join(os.getcwd().split('/')[-3:]))
avg_scores.append(np.mean(scores[-10:]))
if self.plot_option == 'inline':
scores.append(score)
epsilons.append(self.epsilon)
self._plot(frame_idx, scores, losses, epsilons)
elif self.plot_option == 'wandb':
wandb.log({
'Score': score,
'loss(10 frames avg)': np.mean(losses[-10:]),
'Epsilon': self.epsilon
})
print(score, end='\r')
else:
print(score, end='\r')
score = 0
state = self.get_init_state()
history_store = []
else:
state = next_state
self._epsilon_step()
print("Total training time: {}(hrs)".format(
(time.time() - tic) / 3600))
def _epsilon_step(self):
''' Epsilon decay control '''
eps_decay_init = 1 / 1200000
eps_decay = [
eps_decay_init, eps_decay_init / 2.5, eps_decay_init / 3.5,
eps_decay_init / 5.5
]
if self.epsilon > 0.35:
self.epsilon = max(self.epsilon - eps_decay[0], 0.1)
elif self.epsilon > 0.27:
self.epsilon = max(self.epsilon - eps_decay[1], 0.1)
elif self.epsilon > 1.7:
self.epsilon = max(self.epsilon - eps_decay[2], 0.1)
else:
self.epsilon = max(self.epsilon - eps_decay[3], 0.1)
def _compute_loss(self, batch: "Dictionary (S, A, R', S', Dones)"):
# If normalization is used, it must be applied to 'state' and 'next_state' here. ex) state/255
states = torch.FloatTensor(batch['states']).to(self.device) / 255
next_states = torch.FloatTensor(batch['next_states']).to(
self.device) / 255
actions = torch.LongTensor(batch['actions'].reshape(-1,
1)).to(self.device)
rewards = torch.FloatTensor(batch['rewards'].reshape(-1, 1)).to(
self.device)
dones = torch.FloatTensor(batch['dones'].reshape(-1,
1)).to(self.device)
current_q = self.q_current(states).gather(1, actions)
# The next line is the only difference from Vanila DQN.
next_q = self.q_target(next_states).gather(
1,
self.q_current(next_states).argmax(axis=1, keepdim=True)).detach()
mask = 1 - dones
target = (rewards + (mask * self.gamma * next_q)).to(self.device)
loss = F.smooth_l1_loss(current_q, target)
return loss
def _plot(self, frame_idx, scores, losses, epsilons):
clear_output(True)
plt.figure(figsize=(20, 5), facecolor='w')
plt.subplot(131)
plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))
plt.plot(scores)
plt.subplot(132)
plt.title('loss')
plt.plot(losses)
plt.subplot(133)
plt.title('epsilons')
plt.plot(epsilons)
plt.show()
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.0, training_mode=True):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
if training_mode is True:
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
action = np.argmax(action_values.cpu().data.numpy())
else:
action = random.choice(np.arange(self.action_size))
action = np.int32(action)
return action
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class DQN(object):
def __init__(self,state_space,action_space,seed,update_every,batch_size,buffer_size,learning_rate):
self.action_space = action_space
self.state_space = state_space
self.seed = random.seed(seed)
self.batch_size = batch_size
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.update_every = update_every
self.qnetwork_local = QNetwork(state_space,action_space)
self.qnetwork_target = QNetwork(state_space,action_space)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),lr=learning_rate)
# Initialize replaybuffer
self.memory = ReplayBuffer(action_space,buffer_size,buffer_size,seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self,state,action,reward,next_state,done,GAMMA):
# Save the experience
self.memory.add_experience(state,action,reward,next_state,done)
# learn from the experience
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
if len(self.memory) > self.buffer_size:
experiences = self.memory.sample()
self.learn(experiences,GAMMA)
def act(self,state,eps=0.):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.sample(np.arange(self.action_space))
def learn(self,experiences,GAMMA):
states,actions,rewards,next_states,dones = experiences
target_values = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
targets = reward + (GAMMA * target_values * (1-done))
action_values = self.qnetwork_local(states).gather(1,actions)
loss = F.mse_loss(action_values,targets)
loss.backward()
self.optimizer.step()
soft_update(TAU)
def soft_update(self,tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
"""
for local_param,target_param in zip(self.qnetwork_local.parameters(),self.qnetwork_target.parameters()):
local_param.data.copy_(tau*local_param.data + (1-tau)*target_param.data)
# self.qnetwork_local.parameters() = TAU*self.qnetwork_local.parameters() + (1-TAU)*self.qnetwork_target.parameters()
class DQN_Agent():
""" Interacts an learns from the environment. """
def __init__(self,
state_size,
action_size,
seed,
GAMMA=GAMMA,
TAU=TAU,
LR=LR,
UPDATE_EVERY=UPDATE_EVERY,
BUFFER_SIZE=BUFFER_SIZE,
BATCH_SIZE=BATCH_SIZE):
""" Initialize the agent.
==========
PARAMETERS
==========
state_size (int) = observation dimension of the environment
action_size (int) = dimension of each action
seed (int) = random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.gamma = GAMMA
self.tau = TAU
self.lr = LR
self.update_every = UPDATE_EVERY
self.buffer_size = BUFFER_SIZE
self.batch_size = BATCH_SIZE
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
# instantiate online local and target network for weight updates
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(self.device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(self.device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=self.lr)
# create a replay buffer
self.memory = ReplayBuffer(action_size, self.buffer_size,
self.batch_size, seed, self.device)
# time steps for updating target network every time t_step % 4 == 0
self.t_step = 0
def step(self, state, action, reward, next_state, done):
''' Append a SARS sequence to memory, then every update_every steps learn from experiences'''
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# in case enough samples are available in internal memory, sample and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences, self.gamma)
def act(self, state, eps=0.):
""" Choose action from an epsilon-greedy policy
==========
PARAMETERS
==========
state (array) = current state space
eps (float) = epsilon, for epsilon-greedy action choice """
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local.forward(state)
self.qnetwork_local.train()
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
""" Update the value parameters using experience tuples sampled from ReplayBuffer
==========
PARAMETERS
==========
experiences = Tuple of torch.Variable: SARS', done
gamma (float) = discount factor to weight rewards
"""
states, actions, rewards, next_states, dones = experiences
# calculate max predicted Q values for the next states using target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# calculate expected Q vaues from the local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# compute MSE Loss
loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.soft_update(self.qnetwork_local, self.qnetwork_target, self.tau)
def soft_update(self, local_model, target_model, tau):
""" Soft update for model parameters, every update steps as defined above
theta_target = tau * theta_local + (1-tau)*theta_target
==========
PARAMETERS
==========
local_model, target_model = PyTorch Models, weights will be copied from-to
tau = interpolation parameter, type=float
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(self.tau * local_param.data +
(1.0 - self.tau) * target_param.data)
