text: import random
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  
  from qnetwork.model import Network
  
  class Agent():
      '''Interacts with and learns from the environment'''
      
      def __init__(
          self, device, memory, action_size, state_size, 
          batch_size,  gamma,  
          lr, update_every, tau, hidden_layer_lengths
      ):
          '''Initialize an Agent object.
          
          Params
          ======
              device (object): inidicates the device (GPU, CPU) to run the model on
              state_size (int): dimension of each state
              action_size (int): dimension of each action
              batch_size (int): minibatch size
              gamma (float): discount factor
              ir (float): learning rate 
              tau = interpolation parameter
              hidden_layer_lengths = lengths to initialize hidden layers of models
          '''
          self.device = device
          self.action_size = action_size
          self.state_size = state_size
          self.batch_size = batch_size
          self.gamma = gamma
          self.update_every = update_every
          self.tau = tau
          
          
          # Q-Network
          self.qnetwork_local = Network(state_size, action_size, hidden_layer_lengths).to(device)
          self.qnetwork_target = Network(state_size, action_size, hidden_layer_lengths).to(device)
          self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=lr)
          
          # Replay memory
          self.memory = memory
          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) % self.update_every
          if self.t_step == 0:
              if len(self.memory) > self.batch_size:
                  experiences = self.memory.sample()
                  self.learn(experiences)
                  
      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(self.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):
          '''Update value parameters using given batch of experience tuples.
  
          Params
          ======
              experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
          '''
          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 + (self.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)
          
      def soft_update(self, local_model, target_model):
          """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 
          """
          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)
              