class Replay_buffer():
     '''
    Code based on:
    https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py
    Expects tuples of (state, next_state, action, reward, done)
    '''
     def __init__(self, max_size=capacity):
         """Create Replay buffer.
        Parameters
        ----------
        size: int
            Max number of transitions to store in the buffer. When the buffer
            overflows the old memories are dropped.
        """
         self.storage = []
         self.max_size = max_size
         self.ptr = 0
 
     def push(self, data):
         if len(self.storage) == self.max_size:
             self.storage[int(self.ptr)] = data
             self.ptr = (self.ptr + 1) % self.max_size
         else:
             self.storage.append(data)
 
     def sample(self, batch_size):
         """Sample a batch of experiences.
        Parameters
        ----------
        batch_size: int
            How many transitions to sample.
        Returns
        -------
        state: np.array
            batch of state or observations
        action: np.array
            batch of actions executed given a state
        reward: np.array
            rewards received as results of executing action
        next_state: np.array
            next state next state or observations seen after executing action
        done: np.array
            done[i] = 1 if executing ation[i] resulted in
            the end of an episode and 0 otherwise.
        """
         ind = np.random.randint(0, len(self.storage), size=batch_size)
         state, next_state, action, reward, done = [], [], [], [], []
 
         for i in ind:
             st, n_st, act, rew, dn = self.storage[i]
             state.append(np.array(st, copy=False))
             next_state.append(np.array(n_st, copy=False))
             action.append(np.array(act, copy=False))
             reward.append(np.array(rew, copy=False))
             done.append(np.array(dn, copy=False))
 
         return np.array(state), np.array(next_state), np.array(action), np.array(reward).reshape(-1, 1), np.array(done).reshape(-1, 1)

class Actor(nn.Module):
     """
    The Actor model takes in a state observation as input and
    outputs an action, which is a continuous value.
     
    It consists of four fully connected linear layers with ReLU activation functions and
    a final output layer selects one single optimized action for the state
    """
     def __init__(self, n_states, action_dim, hidden1):
         super(Actor, self).__init__()
         self.net = nn.Sequential(
             nn.Linear(n_states, hidden1),
             nn.ReLU(),
             nn.Linear(hidden1, hidden1),
             nn.ReLU(),
             nn.Linear(hidden1, hidden1),
             nn.ReLU(),
             nn.Linear(hidden1, 1)
        )
         
     def forward(self, state):
         return self.net(state)
 
 class Critic(nn.Module):
     """
    The Critic model takes in both a state observation and an action as input and
    outputs a Q-value, which estimates the expected total reward for the current state-action pair.
     
    It consists of four linear layers with ReLU activation functions,
    State and action inputs are concatenated before being fed into the first linear layer.
     
    The output layer has a single output, representing the Q-value
    """
     def __init__(self, n_states, action_dim, hidden2):
         super(Critic, self).__init__()
         self.net = nn.Sequential(
             nn.Linear(n_states + action_dim, hidden2),
             nn.ReLU(),
             nn.Linear(hidden2, hidden2),
             nn.ReLU(),
             nn.Linear(hidden2, hidden2),
             nn.ReLU(),
             nn.Linear(hidden2, action_dim)
        )
         
     def forward(self, state, action):
         return self.net(torch.cat((state, action), 1))

import numpy as np
 import random
 import copy
 
 class OU_Noise(object):
     """Ornstein-Uhlenbeck process.
    code from :
    https://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
    The OU_Noise class has four attributes
     
        size: the size of the noise vector to be generated
        mu: the mean of the noise, set to 0 by default
        theta: the rate of mean reversion, controlling how quickly the noise returns to the mean
        sigma: the volatility of the noise, controlling the magnitude of fluctuations
    """
     def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
         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.
        This method uses the current state of the noise and generates the next sample
        """
         dx = self.theta * (self.mu - self.state) + self.sigma * np.array([np.random.normal() for _ in range(len(self.state))])
         self.state += dx
         return self.state

#Set Hyperparameters
 # Hyperparameters adapted for performance from
 capacity=1000000
 batch_size=64
 update_iteration=200
 tau=0.001 # tau for soft updating
 gamma=0.99 # discount factor
 directory = './'
 hidden1=20 # hidden layer for actor
 hidden2=64. #hiiden laye for critic
 
 class DDPG(object):
     def __init__(self, state_dim, action_dim):
         """
        Initializes the DDPG agent.
        Takes three arguments:
                state_dim which is the dimensionality of the state space,
                action_dim which is the dimensionality of the action space, and
                max_action which is the maximum value an action can take.
         
        Creates a replay buffer, an actor-critic networks and their corresponding target networks.
        It also initializes the optimizer for both actor and critic networks alog with
        counters to track the number of training iterations.
        """
         self.replay_buffer = Replay_buffer()
         
         self.actor = Actor(state_dim, action_dim, hidden1).to(device)
         self.actor_target = Actor(state_dim, action_dim,  hidden1).to(device)
         self.actor_target.load_state_dict(self.actor.state_dict())
         self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=3e-3)
 
         self.critic = Critic(state_dim, action_dim,  hidden2).to(device)
         self.critic_target = Critic(state_dim, action_dim,  hidden2).to(device)
         self.critic_target.load_state_dict(self.critic.state_dict())
         self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=2e-2)
         # learning rate
 
         
 
         self.num_critic_update_iteration = 0
         self.num_actor_update_iteration = 0
         self.num_training = 0
 
     def select_action(self, state):
         """
        takes the current state as input and returns an action to take in that state.
        It uses the actor network to map the state to an action.
        """
         state = torch.FloatTensor(state.reshape(1, -1)).to(device)
         return self.actor(state).cpu().data.numpy().flatten()
 
 
     def update(self):
         """
        updates the actor and critic networks using a batch of samples from the replay buffer.
        For each sample in the batch, it computes the target Q value using the target critic network and the target actor network.
        It then computes the current Q value
        using the critic network and the action taken by the actor network.
         
        It computes the critic loss as the mean squared error between the target Q value and the current Q value, and
        updates the critic network using gradient descent.
         
        It then computes the actor loss as the negative mean Q value using the critic network and the actor network, and
        updates the actor network using gradient ascent.
         
        Finally, it updates the target networks using
        soft updates, where a small fraction of the actor and critic network weights are transferred to their target counterparts.
        This process is repeated for a fixed number of iterations.
        """
 
         for it in range(update_iteration):
             # For each Sample in replay buffer batch
             state, next_state, action, reward, done = self.replay_buffer.sample(batch_size)
             state = torch.FloatTensor(state).to(device)
             action = torch.FloatTensor(action).to(device)
             next_state = torch.FloatTensor(next_state).to(device)
             done = torch.FloatTensor(1-done).to(device)
             reward = torch.FloatTensor(reward).to(device)
 
             # Compute the target Q value
             target_Q = self.critic_target(next_state, self.actor_target(next_state))
             target_Q = reward + (done * gamma * target_Q).detach()
 
             # Get current Q estimate
             current_Q = self.critic(state, action)
 
             # Compute critic loss
             critic_loss = F.mse_loss(current_Q, target_Q)
             
             # Optimize the critic
             self.critic_optimizer.zero_grad()
             critic_loss.backward()
             self.critic_optimizer.step()
 
             # Compute actor loss as the negative mean Q value using the critic network and the actor network
             actor_loss = -self.critic(state, self.actor(state)).mean()
 
             # Optimize the actor
             self.actor_optimizer.zero_grad()
             actor_loss.backward()
             self.actor_optimizer.step()
 
             
             """
            Update the frozen target models using
            soft updates, where
            tau,a small fraction of the actor and critic network weights are transferred to their target counterparts.
            """
             for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
                 target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
 
             for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
                 target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
             
           
             self.num_actor_update_iteration += 1
             self.num_critic_update_iteration += 1
     def save(self):
         """
        Saves the state dictionaries of the actor and critic networks to files
        """
         torch.save(self.actor.state_dict(), directory + 'actor.pth')
         torch.save(self.critic.state_dict(), directory + 'critic.pth')
 
     def load(self):
         """
        Loads the state dictionaries of the actor and critic networks to files
        """
         self.actor.load_state_dict(torch.load(directory + 'actor.pth'))
         self.critic.load_state_dict(torch.load(directory + 'critic.pth'))

import gym
 
 # create the environment
 env_name='MountainCarContinuous-v0'
 env = gym.make(env_name)
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 # Define different parameters for training the agent
 max_episode=100
 max_time_steps=5000
 ep_r = 0
 total_step = 0
 score_hist=[]
 # for rensering the environmnet
 render=True
 render_interval=10
 # for reproducibility
 env.seed(0)
 torch.manual_seed(0)
 np.random.seed(0)
 #Environment action ans states
 state_dim = env.observation_space.shape[0]
 action_dim = env.action_space.shape[0]
 max_action = float(env.action_space.high[0])
 min_Val = torch.tensor(1e-7).float().to(device)
 
 # Exploration Noise
 exploration_noise=0.1
 exploration_noise=0.1 * max_action

# Create a DDPG instance
 agent = DDPG(state_dim, action_dim)
 
 # Train the agent for max_episodes
 for i in range(max_episode):
     total_reward = 0
     step =0
     state = env.reset()
     for  t in range(max_time_steps):
         action = agent.select_action(state)
         # Add Gaussian noise to actions for exploration
         action = (action + np.random.normal(0, 1, size=action_dim)).clip(-max_action, max_action)
         #action += ou_noise.sample()
         next_state, reward, done, info = env.step(action)
         total_reward += reward
         if render and i >= render_interval : env.render()
         agent.replay_buffer.push((state, next_state, action, reward, np.float(done)))
         state = next_state
         if done:
             break
         step += 1
         
     score_hist.append(total_reward)
     total_step += step+1
     print("Episode: \t{} Total Reward: \t{:0.2f}".format( i, total_reward))
     agent.update()
     if i % 10 == 0:
         agent.save()
 env.close()

test_iteration=100
   
 for i in range(test_iteration):
     state = env.reset()
     for t in count():
         action = agent.select_action(state)
         next_state, reward, done, info = env.step(np.float32(action))
         ep_r += reward
         print(reward)
         env.render()
         if done:
             print("reward{}".format(reward))
             print("Episode \t{}, the episode reward is \t{:0.2f}".format(i, ep_r))
             ep_r = 0
             env.render()
             break
         state = next_state