inference.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from dm_control import suite
from torch.distributions import Normal
import matplotlib.pyplot as plt
import os
import torch.nn.functional as F
import time
import random
from torch.autograd import Variable
from collections import deque, OrderedDict
import cv2

class Actor(nn.Module):
  
  def __init__(self, state_dim, action_dim, max_action):
    super(Actor, self).__init__()
    self.layer_1 = nn.Linear(state_dim, 400)
    self.layer_2 = nn.Linear(400, 300)
    self.layer_3 = nn.Linear(300, action_dim)
    self.max_action = max_action

  def forward(self, x):
    x = F.relu(self.layer_1(x))
    x = F.relu(self.layer_2(x))
    x = self.max_action * torch.tanh(self.layer_3(x)) 
    return x

class Critic(nn.Module):
  
  def __init__(self, state_dim, action_dim):
    super(Critic, self).__init__()
    # Defining the first Critic neural network
    self.layer_1 = nn.Linear(state_dim + action_dim, 400)
    self.layer_2 = nn.Linear(400, 300)
    self.layer_3 = nn.Linear(300, 1)
    # Defining the second Critic neural network
    self.layer_4 = nn.Linear(state_dim + action_dim, 400)
    self.layer_5 = nn.Linear(400, 300)
    self.layer_6 = nn.Linear(300, 1)

  def forward(self, x, u):
    xu = torch.cat([x, u], 1)
    # Forward-Propagation on the first Critic Neural Network
    x1 = F.relu(self.layer_1(xu))
    x1 = F.relu(self.layer_2(x1))
    x1 = self.layer_3(x1)
    # Forward-Propagation on the second Critic Neural Network
    x2 = F.relu(self.layer_4(xu))
    x2 = F.relu(self.layer_5(x2))
    x2 = self.layer_6(x2)
    return x1, x2

  def Q1(self, x, u):
    xu = torch.cat([x, u], 1)
    x1 = F.relu(self.layer_1(xu))
    x1 = F.relu(self.layer_2(x1))
    x1 = self.layer_3(x1)
    return x1

# Selecting the device (CPU or GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Building the whole Training Process into a class

class TD3(object):
  
  def __init__(self, state_dim, action_dim, max_action):
    self.actor = Actor(state_dim, action_dim, max_action).to(device)
    self.actor_target = Actor(state_dim, action_dim, max_action).to(device)
    self.actor_target.load_state_dict(self.actor.state_dict())
    self.actor_optimizer = torch.optim.Adam(self.actor.parameters())
    self.critic = Critic(state_dim, action_dim).to(device)
    self.critic_target = Critic(state_dim, action_dim).to(device)
    self.critic_target.load_state_dict(self.critic.state_dict())
    self.critic_optimizer = torch.optim.Adam(self.critic.parameters())
    self.max_action = max_action

  def select_action(self, state):
    state = torch.Tensor(state.reshape(1, -1)).to(device)
    return self.actor(state).cpu().data.numpy().flatten()

  def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2):
    
    for it in range(iterations):
      
      # Step 4: We sample a batch of transitions (s, s’, a, r) from the memory
      batch_states, batch_next_states, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(batch_size)
      state = torch.Tensor(batch_states).to(device)
      next_state = torch.Tensor(batch_next_states).to(device)
      action = torch.Tensor(batch_actions).to(device)
      reward = torch.Tensor(batch_rewards).to(device)
      done = torch.Tensor(batch_dones).to(device)
      
      # Step 5: From the next state s’, the Actor target plays the next action a’
      next_action = self.actor_target(next_state)
      
      # Step 6: We add Gaussian noise to this next action a’ and we clamp it in a range of values supported by the environment
      noise = torch.Tensor(batch_actions).data.normal_(0, policy_noise).to(device)
      noise = noise.clamp(-noise_clip, noise_clip)
      next_action = (next_action + noise).clamp(-self.max_action, self.max_action)
      
      # Step 7: The two Critic targets take each the couple (s’, a’) as input and return two Q-values Qt1(s’,a’) and Qt2(s’,a’) as outputs
      target_Q1, target_Q2 = self.critic_target(next_state, next_action)
      
      # Step 8: We keep the minimum of these two Q-values: min(Qt1, Qt2)
      target_Q = torch.min(target_Q1, target_Q2)
      
      # Step 9: We get the final target of the two Critic models, which is: Qt = r + γ * min(Qt1, Qt2), where γ is the discount factor
      target_Q = reward + ((1 - done) * discount * target_Q).detach()
      
      # Step 10: The two Critic models take each the couple (s, a) as input and return two Q-values Q1(s,a) and Q2(s,a) as outputs
      current_Q1, current_Q2 = self.critic(state, action)
      
      # Step 11: We compute the loss coming from the two Critic models: Critic Loss = MSE_Loss(Q1(s,a), Qt) + MSE_Loss(Q2(s,a), Qt)
      critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q)
      
      # Step 12: We backpropagate this Critic loss and update the parameters of the two Critic models with a SGD optimizer
      self.critic_optimizer.zero_grad()
      critic_loss.backward()
      self.critic_optimizer.step()
      
      # Step 13: Once every two iterations, we update our Actor model by performing gradient ascent on the output of the first Critic model
      if it % policy_freq == 0:
        actor_loss = -self.critic.Q1(state, self.actor(state)).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()
        
        # Step 14: Still once every two iterations, we update the weights of the Actor target by polyak averaging
        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)
        
        # Step 15: Still once every two iterations, we update the weights of the Critic target by polyak averaging
        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)
  
  # Making a save method to save a trained model
  def save(self, filename, directory):
    torch.save(self.actor.state_dict(), '%s/%s_actor.pth' % (directory, filename))
    torch.save(self.critic.state_dict(), '%s/%s_critic.pth' % (directory, filename))
  
  # Making a load method to load a pre-trained model
  def load(self, filename, directory):
    self.actor.load_state_dict(torch.load('%s/%s_actor.pth' % (directory, filename)))
    self.critic.load_state_dict(torch.load('%s/%s_critic.pth' % (directory, filename)))



def process_state(state):
    if isinstance(state, OrderedDict):
        if 'orientations' in state and 'height' in state and 'velocity' in state:
            orient = state['orientations']
            height = state['height']
            velocity = state['velocity']
            if np.isscalar(height):
                height = np.array([height])
            out = np.concatenate((orient, height, velocity))
            return out
    elif isinstance(state, np.ndarray) and state.shape == (24,):
        return state
    elif hasattr(state, 'observation') and isinstance(state.observation, OrderedDict):
        observation = state.observation
        if 'orientations' in observation and 'height' in observation and 'velocity' in observation:
            orient = observation['orientations']
            height = observation['height']
            velocity = observation['velocity']
            if np.isscalar(height):
                height = np.array([height])
            out = np.concatenate((orient, height, velocity))
            return out
    else:
        raise ValueError("Input state must be either an OrderedDict with keys 'orientations', 'height', and 'velocity', a numpy ndarray of shape (24,), or a TimeStep object with a valid observation.")


# Function to visualize the environment
def visualize(env):
    frameA = np.hstack([env.physics.render(480, 480, camera_id=0),
                        env.physics.render(480, 480, camera_id=1)])
    return frameA

def writeMovie(frames):
    # Define the codec and create a VideoWriter object
    height, width, _ = frames[0].shape
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter('walker_stand_animation.mp4', fourcc, 20.0, (width, height))

    # Write the frames to the video file
    for frame in frames:
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        out.write(frame)

    # Release the video writer
    out.release()
    return 


def evaluate_policy(policy, eval_episodes=10):
  frames = []
  avg_reward = 0.
  for _ in range(eval_episodes):
    state = env.reset()
    obs = process_state(state)

    done = False
    while not done:
      action = policy.select_action(np.array(obs))
      state = env.step(action)
      frame = visualize(env)
      frames.append(frame)
      obs = process_state(state)
      reward = state.reward
      done = state.last()
      avg_reward += reward
  avg_reward /= eval_episodes
  print ("---------------------------------------")
  print ("Average Reward over the Evaluation Step: %f" % (avg_reward))
  print ("---------------------------------------")
  return avg_reward, frames

domainName = "walker" # Name of a environment (set it to any Continous environment you want)
taskName = "stand" # Name of a environment (set it to any Continous environment you want)
env_name = domainName+ "_"+taskName
seed = 0 # Random seed number
     
file_name = "%s_%s_%s" % ("TD3", env_name, str(seed))
print ("---------------------------------------")
print ("Settings: %s" % (file_name))
print ("---------------------------------------")

eval_episodes = 10
save_env_vid = True
env = suite.load(domain_name=domainName, task_name=taskName, task_kwargs={'random': seed})


# if save_env_vid:
#   env = wrappers.Monitor(env, monitor_dir, force = True)
#   env.reset()


torch.manual_seed(seed)
np.random.seed(seed)

obsSpec = env.observation_spec()
action_spec = env.action_spec()
orientDim = obsSpec['orientations'].shape[0]
heightDim = len(obsSpec['height'].shape) + 1 
velocityDim = obsSpec['velocity'].shape[0]
input_dim = orientDim + heightDim + velocityDim



state_dim = input_dim
action_dim = action_spec.shape[0]
max_action = float(1)

policy = TD3(state_dim, action_dim, max_action)
policy.load(file_name, './pytorch_models/')

avgReward, frames = evaluate_policy(policy, eval_episodes=eval_episodes)
writeMovie(frames)