class DDQNAgent(Agent):
def __init__(self, *args, **kwargs):
super(DDQNAgent, self).__init__(*args, **kwargs)
self.q_eval = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
'_q_eval',
checkpoint_dir=self.checkpoint_dir)
self.q_next = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
'_q_next',
checkpoint_dir=self.checkpoint_dir)
def choose_action(self, observation):
if np.random.random() > self.epsilon:
state = T.tensor([observation],
dtype=T.float).to(self.q_eval.device)
actions = self.q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
self.q_eval.optimizer.zero_grad()
self.replace_target_network()
states, actions, rewards, new_states, dones = self.sample_memory()
indices = np.arange(self.batch_size)
q_pred = self.q_eval.forward(states)[indices, actions]
q_next = self.q_next.forward(new_states)
q_eval = self.q_eval.forward(new_states)
max_actions = T.argmax(q_eval, dim=1)
q_next[dones] = 0.0
q_target = rewards + self.gamma * q_next[indices, max_actions]
loss = self.q_eval.loss(q_target, q_pred).to(self.q_eval.device)
loss.backward()
self.q_eval.optimizer.step()
self.learn_step_cntr += 1
self.decrement_epsilon()
class DQNAgent(Agent):
def __init__(self, *args, **kwargs):
super(DQNAgent, self).__init__(*args, **kwargs)
self.q_eval = DeepQNetwork(self.n_actions, input_dims=self.input_dims, name=Config.get_name('_q_eval'))
self.q_next = DeepQNetwork(self.n_actions, input_dims=self.input_dims, name=Config.get_name('_q_next'))
def choose_action(self, observation):
if np.random.random() > self.epsilon:
state = T.tensor([observation],dtype=T.float).to(self.q_eval.device)
actions = self.q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action
def learn(self):
if self.memory.mem_cntr < Config.batch_size:
return
self.q_eval.optimizer.zero_grad()
self.replace_target_network()
states, actions, rewards, states_, dones = self.sample_memory()
indices = np.arange(Config.batch_size)
q_pred = self.q_eval.forward(states)[indices, actions]
q_next = self.q_next.forward(states_).max(dim=1)[0]
q_next[dones] = 0.0
q_target = rewards + Config.gamma*q_next
loss = self.q_eval.loss(q_target, q_pred).to(self.q_eval.device)
loss.backward()
self.q_eval.optimizer.step()
self.learn_step_counter += 1
self.decay_epsilon()
class DQNAgent():
def __init__(self, gamma, epsilon, lr, n_actions, input_dims, mem_size, batch_size, eps_min=0.01, eps_dec = 5e-7,
replace =1000, algo=None, env_name = None, chkpt_dir = 'tmp/dqn'):
self.gamma = gamma
self.epsilon = epsilon
self.lr = lr
self.n_actions = n_actions
self.input_dims = input_dims
self.batch_size = batch_size
self.eps_min = eps_min
self.eps_dec = eps_dec
self.replace_target_cnt = replace
self.algo = algo
self.env_name = env_name
self.chkpt_dir = chkpt_dir
self.action_space = [i for i in range(self.n_actions)]
self.learn_step_counter= 0
self.memory = ReplayBuffer(mem_size, input_dims, n_actions)
self.q_eval = DeepQNetwork(self.lr, self.n_actions, input_dims = self.input_dims,name = self.env_name + self.algo + '_q_eval', chkpt_dir= self.chkpt_dir)
self.q_next = DeepQNetwork(self.lr, self.n_actions, input_dims = self.input_dims,name = self.env_name + self.algo + '_q_next', chkpt_dir= self.chkpt_dir)
def choose_action(self, observation):
if(np.random.random()> self.epsilon):
state = T.tensor([observation], dtype=T.float).to(self.q_eval.device)
actions = self.q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action
def store_transition(self, state, action, reward, state_, done):
self.memory.store_transition(state, action, reward, state_, done)
def sample_memory(self):
state, action, reward, new_state, done = self.memory.sample_buffer(self.batch_size)
states = T.tensor(state).to(self.q_eval.device)
rewards= T.tensor(reward).to(self.q_eval.device)
dones = T.tensor(done).to(self.q_eval.device)
actions = T.tensor(action).to(self.q_eval.device)
states_ = T.tensor(new_state).to(self.q_eval.device)
return states, actions, rewards, states_, done
def replace_target_network(self):
if self.learn_step_counter % self.replace_target_cnt == 0:
self.q_next.load_state_dict(self.q_eval.state_dict())
def decrement_epsilon(self):
self.epsilon = self.epsilon -self.eps_dec if self.epsilon > self.eps_min else self.eps_min
def save_models(self):
self.q_eval.save_checkpoint()
self.q_next.save_checkpoint()
def load_models(self):
self.q_eval.load_checkpoint()
self.q_next.load_checkpoint()
def learn(self):
if(self.memory.mem_cntr < self.batch_size):
return
self.q_eval.optimizer.zero_grad()
self.replace_target_network()
states, actions, rewards, states_, dones = self.sample_memory()
indices = np.arange(self.batch_size)
q_pred = self.q_eval.forward(states)[indices, actions]
q_next = self.q_next.forward(states_).max(dim=1)[0]
q_next[dones] = 0
q_target= rewards + self.gamma * q_next
loss =self.q_eval.loss(q_target, q_pred).to(self.q_eval.device)
loss.backward()
self.q_eval.optimizer.step()
self.learn_step_counter+=1
self.decrement_epsilon()
class DQNAgent(object):
def __init__(self,
gamma,
epsilon,
lr,
n_actions,
input_dims,
mem_size,
batch_size,
eps_min=0.01,
eps_dec=5e-7,
replace=1000,
algo=None,
env_name=None,
chkpt_dir='tmp/dqn'):
self.gamma = gamma
self.epsilon = epsilon
self.lr = lr
self.n_actions = n_actions
self.input_dims = input_dims
self.batch_size = batch_size
self.eps_min = eps_min
self.eps_dec = eps_dec
self.replace_target_cnt = replace
self.algo = algo
self.env_name = env_name
self.chkpt_dir = chkpt_dir
self.action_space = [i for i in range(n_actions)]
self.learn_step_counter = 0
self.memory = ReplayBuffer(mem_size, input_dims, n_actions)
self.q_eval = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
'_q_eval',
chkpt_dir=self.chkpt_dir)
self.q_next = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
'_q_next',
chkpt_dir=self.chkpt_dir)
#self.dim_bechir = self.q_eval.calculate_output_bechir(self.input_dims)
def choose_action(self, observation):
"""
Choose an action through an epsilon-greedy approach.
:param observation: state features as provided by gym environment.
:return: action
"""
if np.random.random() > self.epsilon:
# Convert state to Pytorch tensor and send to q_eval.device
state = T.tensor([observation],
dtype=T.float).to(self.q_eval.device)
# Get actions values from q_eval network
actions = self.q_eval.forward(state)
# Get action with highest value
action = T.argmax(actions).item()
else:
# Select random action from action space
action = np.random.choice(self.action_space)
return action
def store_transition(self, state, action, reward, state_, done):
self.memory.store_transition(state, action, reward, state_, done)
def sample_memory(self):
state, action, reward, new_state, done = \
self.memory.sample_buffer(self.batch_size)
states = T.tensor(state).to(self.q_eval.device)
rewards = T.tensor(reward).to(self.q_eval.device)
dones = T.tensor(done).to(self.q_eval.device)
actions = T.tensor(action).to(self.q_eval.device)
states_ = T.tensor(new_state).to(self.q_eval.device)
return states, actions, rewards, states_, dones
def replace_target_network(self):
if self.learn_step_counter % self.replace_target_cnt == 0:
self.q_next.load_state_dict(self.q_eval.state_dict())
def decrement_epsilon(self):
self.epsilon = self.epsilon - self.eps_dec \
if self.epsilon > self.eps_min else self.eps_min
def save_models(self):
self.q_eval.save_checkpoint()
self.q_next.save_checkpoint()
def load_models(self):
self.q_eval.load_checkpoint()
self.q_next.load_checkpoint()
def learn(self):
# If memory counter has not reached batch size simply return
if self.memory.mem_cntr < self.batch_size:
return
# reset gradients for the main network's optimizer
self.q_eval.optimizer.zero_grad()
# Call function to update target network weights every n steps
self.replace_target_network()
# Sample environment transitions from the replay buffer
states, actions, rewards, states_, dones = self.sample_memory()
# Get Q(s,a) for the actions performed by the agent.
# Because we processed a batch of states, we need to index the result of the forward function by the indices of
# the states (from 0 to batch_size) followed by the index of the action performed by the agent.
indices = np.arange(self.batch_size)
q_pred = self.q_eval.forward(states)[indices, actions]
# Get max Q(s', a') from target network
q_next = self.q_next.forward(states_).max(dim=1)[0]
# Set Q(s', a') to zero for terminal states
q_next[dones] = 0.0
# Compute the q_target as r + gamma * Q(s',a')
q_target = rewards + self.gamma * q_next
# Compute the loss tensor and move it to q_eval.device
loss = self.q_eval.loss(q_target, q_pred).to(self.q_eval.device)
# Backpropagate loss and optimize network parameters
loss.backward()
self.q_eval.optimizer.step()
# Increment training counter
self.learn_step_counter += 1
# Decrement epsilon for epsilon-greedy action selection
self.decrement_epsilon()
class DQNAgent():
def __init__(self,
gamma,
epsilon,
lr,
n_actions,
input_dims,
mem_size,
batch_size,
chkpt_dir,
eps_min=0.01,
eps_dec=5e-7,
replace=1000,
algo=None,
env_name=None):
self.gamma = gamma # 0.99
self.epsilon = epsilon # 1.0
self.lr = lr # 0.0001
self.n_actions = n_actions # 6
self.input_dims = input_dims # (4, 84, 84)
self.batch_size = batch_size # 32
self.eps_min = eps_min # 0.1
self.eps_dec = eps_dec # 1e-05
self.replace_target_cnt = replace # 1000
self.algo = algo # 'DQNAgent'
self.env_name = env_name # 'PongNoFrameskip-v4'
self.chkpt_dir = chkpt_dir # .\\models\\
self.action_space = [i for i in range(self.n_actions)
] # [0, 1, 2, 3, 4, 5]
self.learn_step_counter = 0
self.memory = ReplayBuffer(mem_size, input_dims, n_actions)
self.q_eval = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
'_q_eval',
chkpt_dir=self.chkpt_dir)
self.q_next = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
'_q_next',
chkpt_dir=self.chkpt_dir)
def choose_action(self, observation):
if np.random.random() > self.epsilon:
state = T.tensor([observation],
dtype=T.float).to(self.q_eval.device)
actions = self.q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action
def store_transition(self, state, action, reward, state_, done):
self.memory.store_transition(state, action, reward, state_, done)
def sample_memory(self):
state, action, reward, new_state, done = self.memory.sample_buffer(
self.batch_size)
states = T.tensor(state).to(self.q_eval.device)
rewards = T.tensor(reward).to(self.q_eval.device)
dones = T.tensor(done).to(self.q_eval.device)
actions = T.tensor(action).to(self.q_eval.device)
states_ = T.tensor(new_state).to(self.q_eval.device)
return states, actions, rewards, states_, dones
def replace_target_network(self):
if self.learn_step_counter % self.replace_target_cnt == 0:
self.q_next.load_state_dict(self.q_eval.state_dict(
)) # load_state_dict and state_dict are inbuilt functions of torch
def decrement_epsilon(self):
self.epsilon = self.epsilon - self.eps_dec if self.epsilon > self.eps_min else self.eps_min
def save_models(self):
self.q_eval.save_checkpoint()
self.q_next.save_checkpoint()
def load_models(self):
self.q_eval.load_checkpoint()
self.q_next.load_checkpoint()
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
self.q_eval.optimizer.zero_grad()
self.replace_target_network()
states, actions, rewards, states_, dones = self.sample_memory()
indices = np.arange(self.batch_size)
q_pred = self.q_eval.forward(
states
)[indices,
actions] # self.q_eval.forward(states).shape = (32, 6), q_pred.shape = 32
q_next = self.q_next.forward(states_).max(
dim=1
)[0] # self.q_next.forward(states_).shape = (32, 6), q_next.shape = 32
temp_dones = dones.bool()
q_next[temp_dones] = 0.0 # as reward for terminal state is 0
q_target = rewards + self.gamma * q_next
loss = self.q_eval.loss(q_target, q_pred).to(self.q_eval.device)
loss.backward()
self.q_eval.optimizer.step()
self.learn_step_counter += 1
self.decrement_epsilon()
class Agent(object):
def __init__(self,
gamma,
epsilon,
lr,
n_actions,
input_dims,
mem_size,
batch_size,
eps_min=0.01,
eps_dec=5e-7,
replace=1000,
algo=None,
env_name=None,
checkpoint_dir='tmp/dqn'):
self.gamma = gamma
self.epsilon = epsilon
self.lr = lr
self.n_actions = n_actions
self.input_dims = input_dims
self.batch_size = batch_size
self.eps_min = eps_min
self.eps_dec = eps_dec
self.replace_target_counter = replace
self.algo = algo
self.env_name = env_name
self.checkpoint_dir = checkpoint_dir
self.action_space = [i for i in range(self.n_actions)]
self.learn_step_counter = 0
self.memory = ReplayBuffer(mem_size, input_dims, n_actions)
self.q_eval = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
"_q_eval",
checkpoint_dir=self.checkpoint_dir)
self.q_next = DeepQNetwork(self.lr,
self.n_actions,
input_dims=self.input_dims,
name=self.env_name + '_' + self.algo +
"_q_next",
checkpoint_dir=self.checkpoint_dir)
def store_transition(self, state, action, reward, resulted_state, done):
self.memory.store_transition(state, action, reward, resulted_state,
done)
def sample_memory(self):
state, action, reward, resulted_state, done = self.memory.sample_buffer(
self.batch_size)
states = T.tensor(state).to(self.q_eval.device)
rewards = T.tensor(reward).to(self.q_eval.device)
dones = T.tensor(done).to(self.q_eval.device)
actions = T.tensor(action).to(self.q_eval.device)
resulted_states = T.tensor(resulted_state).to(self.q_eval.device)
return states, actions, rewards, resulted_states, dones
def choose_action(self, observation):
if np.random.random() > self.epsilon:
state = T.tensor([observation], dtype=T.float).to(
self.q_eval.device) # converting observation to tensor,
# and observation is in the list because our convolution expects an input tensor of shape batch size
# by input dims.
_, advantages = self.q_eval.forward(state)
action = T.argmax(advantages).item()
else:
action = np.random.choice(self.action_space)
return action
def replace_target_network(self):
if self.replace_target_counter is not None and \
self.learn_step_counter % self.replace_target_counter == 0:
self.q_next.load_state_dict(self.q_eval.state_dict())
def decrement_epsilon(self):
if self.epsilon > self.eps_min:
self.epsilon = self.epsilon - self.eps_dec
else:
self.epsilon = self.eps_min
def learn(self):
if self.memory.mem_counter < self.batch_size:
return
self.q_eval.optimizer.zero_grad()
self.replace_target_network()
states, actions, rewards, resulted_states, dones = self.sample_memory()
indexes = np.arange(self.batch_size)
V_states, A_states = self.q_eval.forward(states)
q_pred = T.add(
V_states, (A_states - A_states.mean(dim=1, keepdim=True)))[indexes,
actions]
V_resulted_states, A_resulted_states = self.q_next.forward(
resulted_states)
q_next = T.add(
V_resulted_states,
(A_resulted_states -
A_resulted_states.mean(dim=1, keepdim=True))).max(dim=1)[0]
q_next[dones] = 0.0
target = rewards + self.gamma * q_next
loss = self.q_eval.loss(target, q_pred).to(self.q_eval.device)
loss.backward()
self.q_eval.optimizer.step()
self.learn_step_counter += 1
self.decrement_epsilon()
def save_models(self):
self.q_eval.save_checkpoint()
self.q_next.save_checkpoint()
def load_models(self):
self.q_eval.load_checkpoint()
self.q_next.load_checkpoint()