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agent.py
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agent.py
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import torch
import numpy as np
from memory import ReplayBuffer
class DuelingDQN(torch.nn.Module):
def __init__(self, input_shape, n_actions, alpha=3e-4, chkpt_file="weights/q.pt"):
super(DuelingDQN, self).__init__()
self.chkpt_file = chkpt_file
self.conv1 = torch.nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4)
self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=4, stride=2)
self.conv3 = torch.nn.Conv2d(64, 64, kernel_size=3, stride=1)
self.fc1_input_dim = self._calculate_fc1_input_dim(input_shape)
self.fc1 = torch.nn.Linear(self.fc1_input_dim, 512)
self.advantage = torch.nn.Linear(512, n_actions)
self.value = torch.nn.Linear(512, 1)
self.optimizer = torch.optim.RMSprop(self.parameters(), lr=alpha)
self.loss = torch.nn.MSELoss() # use squared l1 instead of mse?
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.to(self.device)
self._initialize_weights()
def forward(self, x):
x = torch.nn.functional.relu(self.conv1(x))
x = torch.nn.functional.relu(self.conv2(x))
x = torch.nn.functional.relu(self.conv3(x))
x = x.view(x.size()[0], -1)
x = torch.nn.functional.relu(self.fc1(x))
a = self.advantage(x)
v = self.value(x)
return v, a
def save_checkpoint(self):
torch.save(self.state_dict(), self.chkpt_file)
def load_checkpoint(self):
self.load_state_dict(torch.load(self.chkpt_file))
def _calculate_fc1_input_dim(self, input_shape):
dummy_input = torch.zeros(1, *input_shape)
x = torch.nn.functional.relu(self.conv1(dummy_input))
x = torch.nn.functional.relu(self.conv2(x))
x = torch.nn.functional.relu(self.conv3(x))
return x.numel()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):
torch.nn.init.orthogonal_(m.weight)
if isinstance(m, torch.nn.Linear):
m.weight.data.mul_(1 / 100)
if m.bias is not None:
torch.nn.init.constant_(m.bias, 0)
class DuelingDDQNAgent:
def __init__(
self,
env_name,
input_shape,
n_actions,
alpha=3e-4,
gamma=0.99,
eps_min=0.1,
eps_dec=5e-7,
batch_size=32,
mem_size=300000,
replace_target_count=1000,
):
self.gamma = gamma
self.epsilon = 1.0
self.eps_min = eps_min
self.eps_dec = eps_dec
self.n_actions = n_actions
self.batch_size = batch_size
self.replace_target_count = replace_target_count
self.counter = 0
self.memory = ReplayBuffer(input_shape, int(mem_size), batch_size)
self.q1 = DuelingDQN(input_shape, n_actions, alpha, f"weights/{env_name}_q1.pt")
self.q2 = DuelingDQN(input_shape, n_actions, alpha, f"weights/{env_name}_q2.pt")
def choose_action(self, state):
if np.random.random() > self.epsilon:
state = torch.FloatTensor(state).unsqueeze(0).to(self.q1.device)
_, advantages = self.q1(state)
return torch.argmax(advantages).cpu().numpy()
return np.random.randint(0, self.n_actions)
def store_transition(self, state, action, reward, next_state, done):
self.memory.store_transition(state, action, reward, next_state, done)
def learn(self):
if self.memory.mem_counter < self.batch_size:
return
if self.counter % self.replace_target_count == 0:
self.update_target_parameters()
states, actions, rewards, next_states, dones = self.memory.sample()
states = torch.FloatTensor(states).to(self.q1.device)
actions = torch.IntTensor(actions).to(self.q1.device)
next_states = torch.FloatTensor(next_states).to(self.q1.device)
rewards = torch.FloatTensor(rewards).to(self.q1.device)
dones = torch.BoolTensor(dones).to(self.q1.device)
self.q1.optimizer.zero_grad()
ids = np.arange(self.batch_size)
v, a = self.q1(states)
q_pred = torch.add(v, (a - a.mean(dim=1, keepdim=True)))[ids, actions]
# get target value of online policy
v_eval, a_eval = self.q1(next_states)
q_eval = torch.add(v_eval, (a_eval - a_eval.mean(dim=1, keepdim=True)))
max_actions = torch.argmax(q_eval, dim=1)
v_t, a_t = self.q2(next_states)
q_target = torch.add(v_t, (a_t - a_t.mean(dim=1, keepdim=True)))
q_target[dones] = 0.0
q_target = rewards + self.gamma * q_target[ids, max_actions]
loss = self.q1.loss(q_target, q_pred).to(self.q1.device)
loss.backward()
self.q1.optimizer.step()
self.counter += 1
self.decrement_epsilon()
def decrement_epsilon(self):
self.epsilon = max(self.eps_min, self.epsilon - self.eps_dec)
def update_target_parameters(self):
self.q2.load_state_dict(dict(self.q1.named_parameters()))
def save_checkpoint(self):
self.q1.save_checkpoint()
self.q2.save_checkpoint()
def load_checkpoint(self):
self.q1.load_checkpoint()
self.q2.load_checkpoint()