import numpy as np
import random
from collections import namedtuple, deque

from dueling_model import Dueling_QNetwork
from model import QNetwork

import torch
import torch.nn.functional as F
import torch.optim as optim

BUFFER_SIZE = int(1e5)  # replay buffer size
BATCH_SIZE = 64  # minibatch size
GAMMA = 0.99  # discount factor
TAU = 1e-3  # for soft update of target parameters
LR = 5e-4  # learning rate
UPDATE_EVERY = 4  # how often to update the network

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


class Double_DQN_Agent():
    """Interacts with and learns from the environment."""

    def __init__(self, state_size, action_size, seed, network_type=None):
        """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)

        # Specify Network

        # Vanilla Q-Network
        if network_type == None:
            print("Using double-DQN network")
            self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
            self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)

        # Dueling Q-Network
        elif network_type == 'Dueling':
            print("Using double-DQN Dueling network")
            self.qnetwork_local = Dueling_QNetwork(state_size, action_size, seed).to(device)
            self.qnetwork_target = Dueling_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.):
        """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)  # same as self.qnetwork_local.forward(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

        # Double DQN,has two q network value, one is qns_local which is used to get the most possible action.
        # but compute the target qsa value through qns_target which value keep the same as last step.
        # only update after the loss backpropagation completed.
        # compared to normal DQN network, it's designed to reduce estimation of qsa value.
        
        # Get the local net next State value
        qns_local = self.qnetwork_local.forward(next_states)
        # Get the argmax action of state of local net
        _, qnsa_local_argmax_a = torch.max(qns_local, dim=1)
        # Get the target net next state value
        qns_target = self.qnetwork_target.forward(next_states)
        # Use the argmax a of local net to get the next state Q in target net
        qnsa_target = qns_target[torch.arange(BATCH_SIZE, dtype=torch.long), qnsa_local_argmax_a.reshape(BATCH_SIZE)]

        # Get the TD target
        qnsa_target = qnsa_target * (1 - dones.reshape(BATCH_SIZE))
        qnsa_target = qnsa_target.reshape((BATCH_SIZE, 1))
        TD_target = rewards + gamma * qnsa_target

        # Get the train value
        qs_local = self.qnetwork_local.forward(states)
        qsa_local = qs_local[torch.arange(BATCH_SIZE, dtype=torch.long), actions.reshape(BATCH_SIZE)]
        qsa_local = qsa_local.reshape((BATCH_SIZE, 1))

        # Backpropagation
        loss = F.mse_loss(qsa_local, TD_target)  # mean square loss

        self.optimizer.zero_grad()  # clears the gradients
        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(state, action, reward, next_state, 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(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(
            device)
        dones = torch.from_numpy(np.vstack([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)
import numpy as np
import random
from collections import namedtuple, deque

from dueling_model import Dueling_QNetwork
from model import QNetwork

import torch
import torch.nn.functional as F
import torch.optim as optim

BUFFER_SIZE = int(1e5)  # replay buffer size
BATCH_SIZE = 64  # minibatch size
GAMMA = 0.99  # discount factor
TAU = 1e-3  # for soft update of target parameters
LR = 5e-4  # learning rate
UPDATE_EVERY = 4  # how often to update the network

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


class Double_DQN_Agent():
    """Interacts with and learns from the environment."""

    def __init__(self, state_size, action_size, seed, network_type=None):
        """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)

        # Specify Network

        # Vanilla Q-Network
        if network_type == None:
            print("Using double-DQN network")
            self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
            self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)

        # Dueling Q-Network
        elif network_type == 'Dueling':
            print("Using double-DQN Dueling network")
            self.qnetwork_local = Dueling_QNetwork(state_size, action_size, seed).to(device)
            self.qnetwork_target = Dueling_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.):
        """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)  # same as self.qnetwork_local.forward(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

        # Double DQN,has two q network value, one is qns_local which is used to get the most possible action.
        # but compute the target qsa value through qns_target which value keep the same as last step.
        # only update after the loss backpropagation completed.
        # compared to normal DQN network, it's designed to reduce estimation of qsa value.
        
        # Get the local net next State value
        qns_local = self.qnetwork_local.forward(next_states)
        # Get the argmax action of state of local net
        _, qnsa_local_argmax_a = torch.max(qns_local, dim=1)
        # Get the target net next state value
        qns_target = self.qnetwork_target.forward(next_states)
        # Use the argmax a of local net to get the next state Q in target net
        qnsa_target = qns_target[torch.arange(BATCH_SIZE, dtype=torch.long), qnsa_local_argmax_a.reshape(BATCH_SIZE)]

        # Get the TD target
        qnsa_target = qnsa_target * (1 - dones.reshape(BATCH_SIZE))
        qnsa_target = qnsa_target.reshape((BATCH_SIZE, 1))
        TD_target = rewards + gamma * qnsa_target

        # Get the train value
        qs_local = self.qnetwork_local.forward(states)
        qsa_local = qs_local[torch.arange(BATCH_SIZE, dtype=torch.long), actions.reshape(BATCH_SIZE)]
        qsa_local = qsa_local.reshape((BATCH_SIZE, 1))

        # Backpropagation
        loss = F.mse_loss(qsa_local, TD_target)  # mean square loss

        self.optimizer.zero_grad()  # clears the gradients
        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(state, action, reward, next_state, 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(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(
            device)
        dones = torch.from_numpy(np.vstack([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)
# replay buffer size # minibatch size # discount factor # for soft update of target parameters # learning rate # how often to update the network Interacts with and learns from the environment. Initialize an Agent object.          Params         ======             state_size (int): dimension of each state             action_size (int): dimension of each action             seed (int): random seed # Specify Network # Vanilla Q-Network # Dueling Q-Network # Replay memory # Initialize time step (for updating every UPDATE_EVERY steps) # Save experience in replay memory # Learn every UPDATE_EVERY time steps. # If enough samples are available in memory, get random subset and learn Returns actions for given state as per current policy.          Params         ======             state (array_like): current state             eps (float): epsilon, for epsilon-greedy action selection # same as self.qnetwork_local.forward(state) # Epsilon-greedy action selection 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 # Double DQN,has two q network value, one is qns_local which is used to get the most possible action. # but compute the target qsa value through qns_target which value keep the same as last step. # only update after the loss backpropagation completed. # compared to normal DQN network, it's designed to reduce estimation of qsa value. # Get the local net next State value # Get the argmax action of state of local net # Get the target net next state value # Use the argmax a of local net to get the next state Q in target net # Get the TD target # Get the train value # Backpropagation # mean square loss # clears the gradients # ------------------- update target network ------------------- # 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 Fixed-size buffer to store experience tuples. 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 Add a new experience to memory. Randomly sample a batch of experiences from memory. Return the current size of internal memory.