text: # This code is a slight modification of code provided by the Udacity instructor team.
  #
  
  import numpy as np
  import random
  import copy
  from collections import namedtuple, deque
  
  from model import Actor, Critic
  
  import torch
  import torch.nn.functional as F
  import torch.optim as optim
  
  BUFFER_SIZE = int(1e6)  # replay buffer size; orig 1e4
  GAMMA = 0.99            # discount factor; orig 0.99
  TAU = 0.001             # for soft update of target parameters; orig 0.001
  LR_ACTOR = 0.001       # learning rate of the actor; orig 0.0001
  LR_CRITIC = 0.001       # learning rate of the critic; orig 0.001
  WEIGHT_DECAY = 1e-5        # L2 weight decay; orig 0
  
  device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
  
  class DdpgAgent():
      """Interacts with and learns from the environment."""
      
      def __init__(self, state_size, action_size, random_seed, batch_size=32,
                   noise_decay=1.0, learn_every=20, learn_iter=10,
                   actor_file=None, critic_file=None):
          """Initialize an Agent object.
          
          Params
          ======
              state_size (int): dimension of each state
              action_size (int): dimension of each action
              random_seed (int): random seed
          """
          self.state_size = state_size
          self.action_size = action_size
          self.seed = random.seed(random_seed)
          self.batch_size = batch_size
          self.noise_mult = 1.0
          self.noise_decay = noise_decay
          self.learn_control = 0
          self.learn_every = learn_every
          self.learn_iterations = learn_iter
          self.inference = False
  
          ##################################
          # Define the NNs. Keep the layer structure identical in all 4, but the critic will merge in the outputs of the actor.
          ##################################
  
          # was using 128, 48
          layer1_units = 400
          layer2_units = 256
  
          # Actor Network (w/ Target Network)
          self.actor_local  = Actor(state_size, action_size, random_seed, fc1_units=layer1_units, fc2_units=layer2_units)
          self.actor_target = Actor(state_size, action_size, random_seed, fc1_units=layer1_units, fc2_units=layer2_units)
          if actor_file != None:
              self.actor_local.load_state_dict(torch.load(actor_file))
              self.actor_target.load_state_dict(torch.load(actor_file))
              self.inference = True #indicate that we will only do inference if this file is pre-loaded
          self.actor_local.to(device)
          self.actor_target.to(device)
          self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)
  
          # Critic Network (w/ Target Network)
          self.critic_local  = Critic(state_size, action_size, random_seed, fcs1_units=layer1_units, fc2_units=layer2_units)
          self.critic_target = Critic(state_size, action_size, random_seed, fcs1_units=layer1_units, fc2_units=layer2_units)
          if critic_file != None:
              self.critic_local.load_state_dict(torch.load(critic_file))
              self.critic_target.load_state_dict(torch.load(critic_file))
          self.critic_local.to(device)
          self.critic_target.to(device)
          self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)
  
          # Noise process
          self.noise = OUNoise(action_size, random_seed)
  
          # Replay memory
          self.memory = ReplayBuffer(action_size, BUFFER_SIZE, self.batch_size, random_seed)
      
      def step(self, state, action, reward, next_state, done):
          """Save experience in replay memory, and use random sample from buffer to learn."""
  
          # don't need to do anything if this is an inference run
          if self.inference:
              return
  
          # Save experience / reward
          self.memory.add(state, action, reward, next_state, done)
  
          ##### John modified this section for infrequent learning updates
          # Learn, if enough samples are available in memory
          self.learn_control += 1
          if len(self.memory) > self.batch_size  and  self.learn_control % self.learn_every == 0:
              for j in range(self.learn_iterations):
                  experiences = self.memory.sample()
                  self.learn(experiences, GAMMA)
  
      def act(self, state, add_noise=True):
          """Returns actions for given state as per current policy."""
          state = torch.from_numpy(state).float().to(device)
          self.actor_local.eval()
          with torch.no_grad():
              action = self.actor_local(state).cpu().data.numpy()
  
          # if we are training then set the model back to training mode and add noise to the actions
          if not self.inference:
              self.actor_local.train()
              if add_noise:
                  action += self.noise.sample() * self.noise_mult
                  self.noise_mult *= self.noise_decay
  
          return np.clip(action, -1, 1)
  
  
      def reset(self):
          self.noise.reset()
  
      def learn(self, experiences, gamma):
          """Update policy and value parameters using given batch of experience tuples.
          Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
          where:
              actor_target(state) -> action
              critic_target(state, action) -> Q-value
  
          Params
          ======
              experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples 
              gamma (float): discount factor
          """
          states, actions, rewards, next_states, dones = experiences
  
          # ---------------------------- update critic ---------------------------- #
  
          # Get predicted next-state actions and Q values from target models
          actions_next = self.actor_target(next_states)
          Q_targets_next = self.critic_target(next_states, actions_next)
  
          # Compute Q targets for current states (y_i)
          Q_targets = rewards + gamma*Q_targets_next*(1 - dones)
  
          # Compute critic loss
          Q_expected = self.critic_local(states, actions)
          critic_loss = F.mse_loss(Q_expected, Q_targets)
  
          # Minimize the loss
          self.critic_optimizer.zero_grad()
          critic_loss.backward()
          torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1) #John added per lesson suggestion; gradient clipping
          self.critic_optimizer.step()
  
          # ---------------------------- update actor ---------------------------- #
  
          # Compute actor loss
          actions_pred = self.actor_local(states)
          actor_loss = -self.critic_local(states, actions_pred).mean() #can't detach() here
  
          # Minimize the loss
          self.actor_optimizer.zero_grad()
          actor_loss.backward()
          torch.nn.utils.clip_grad_norm_(self.actor_local.parameters(), 1) #John added
          self.actor_optimizer.step()
  
          # ----------------------- update target networks ----------------------- #
  
          self.soft_update(self.critic_local, self.critic_target, TAU)
          self.soft_update(self.actor_local, self.actor_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 OUNoise:
      """Ornstein-Uhlenbeck process."""
  
      def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
          """Initialize parameters and noise process."""
          self.mu = mu * np.ones(size)
          self.theta = theta
          self.sigma = sigma
          self.seed = random.seed(seed)
          self.reset()
  
      def reset(self):
          """Reset the internal state (= noise) to mean (mu)."""
          self.state = copy.copy(self.mu)
  
      def sample(self):
          """Update internal state and return it as a noise sample."""
          x = self.state
          dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])
          self.state = x + dx
          return self.state
  
  class ReplayBuffer:
      """Fixed-size buffer to store experience tuples."""
  
      def __init__(self, action_size, buffer_size, batch_size, seed):
          """Initialize a ReplayBuffer object.
          Params
          ======
              buffer_size (int): maximum size of buffer
              batch_size (int): size of each training batch
          """
          self.action_size = action_size
          self.memory = deque(maxlen=buffer_size)  # internal memory (deque)
          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])).float().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)
  
  