import copy
import os
import random
from collections import namedtuple, deque, Iterable
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch_training.model import QNetwork, QNetwork2
BUFFER_SIZE = int(1e5) # replay buffer size
BATCH_SIZE = 512 # minibatch size
GAMMA = 0.99 # discount factor 0.99
TAU = 1e-3 # for soft update of target parameters
LR = 0.5e-4 # learning rate 5
UPDATE_EVERY = 10 # how often to update the network
double_dqn = True # If using double dqn algorithm
input_channels = 5 # Number of Input channels
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
print(device)
class Agent:
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, net_type, seed, double_dqn=True, input_channels=5):
"""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)
self.version = net_type
self.double_dqn = double_dqn
# Q-Network
if self.version == "Conv":
self.qnetwork_local = QNetwork2(state_size, action_size, seed, input_channels).to(device)
self.qnetwork_target = copy.deepcopy(self.qnetwork_local)
else:
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = copy.deepcopy(self.qnetwork_local)
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 save(self, filename):
torch.save(self.qnetwork_local.state_dict(), filename + ".local")
torch.save(self.qnetwork_target.state_dict(), filename + ".target")
def load(self, filename):
if os.path.exists(filename + ".local"):
self.qnetwork_local.load_state_dict(torch.load(filename + ".local"))
if os.path.exists(filename + ".target"):
self.qnetwork_target.load_state_dict(torch.load(filename + ".target"))
def step(self, state, action, reward, next_state, done, train=True):
# 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()
if train:
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""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)
self.qnetwork_local.train()
# Epsilon-greedy action selection
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 value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
if self.double_dqn:
# Double DQN
q_best_action = self.qnetwork_local(next_states).max(1)[1]
Q_targets_next = self.qnetwork_target(next_states).gather(1, q_best_action.unsqueeze(-1))
else:
# DQN
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))
# 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 ReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
def __init__(self, action_size, buffer_size, batch_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed
"""
self.action_size = action_size
self.memory = deque(maxlen=buffer_size)
self.batch_size = batch_size
self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
self.seed = random.seed(seed)
def add(self, state, action, reward, next_state, done):
"""Add a new experience to memory."""
e = self.experience(np.expand_dims(state, 0), action, reward, np.expand_dims(next_state, 0), done)
self.memory.append(e)
def sample(self):
"""Randomly sample a batch of experiences from memory."""
experiences = random.sample(self.memory, k=self.batch_size)
states = torch.from_numpy(self.__v_stack_impr([e.state for e in experiences if e is not None])) \
.float().to(device)
actions = torch.from_numpy(self.__v_stack_impr([e.action for e in experiences if e is not None])) \
.long().to(device)
rewards = torch.from_numpy(self.__v_stack_impr([e.reward for e in experiences if e is not None])) \
.float().to(device)
next_states = torch.from_numpy(self.__v_stack_impr([e.next_state for e in experiences if e is not None])) \
.float().to(device)
dones = torch.from_numpy(self.__v_stack_impr([e.done for e in experiences if e is not None]).astype(np.uint8)) \
.float().to(device)
return (states, actions, rewards, next_states, dones)
def __len__(self):
"""Return the current size of internal memory."""
return len(self.memory)
def __v_stack_impr(self, states):
sub_dim = len(states[0][0]) if isinstance(states[0], Iterable) else 1
np_states = np.reshape(np.array(states), (len(states), sub_dim))
return np_states