RuntimeError: Error(s) in loading state_dict for Actor - torch.load()
I have created a custom environment in open ai gym  and i am facing error while loading the weights Could some one help me to resolve the issue . I am training a TD3 network in a custom environment and i have trained successfully but while inferencing i am facing this issue 

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 evaluate_policy(policy, eval_episodes=10):
  avg_reward = 0.
  for _ in range(eval_episodes):
    obs = env.reset()
    done = False
    while not done:
      action = policy.select_action(np.array(obs))
      obs, reward, done, _ = env.step(action)
      avg_reward += reward
  avg_reward /= eval_episodes
  print ("---------------------------------------")
  print ("Average Reward over the Evaluation Step: %f" % (avg_reward))
  print ("---------------------------------------")
  return avg_reward

env_name = "Pygame-v0"
seed = 0

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 = gym.make(env_name)
max_episode_steps = env._max_episode_steps
if save_env_vid:
  env = wrappers.Monitor(env, monitor_dir, force = True)
  env.reset()
env.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
policy = TD3(state_dim, action_dim, max_action)
#policy.load(file_name, './pytorch_models/')
policy.load(file_name,"/content/gdrive/My Drive/reinforce/gym_game/pytorch_models")
_ = evaluate_policy(policy, eval_episodes=eval_episodes)


Traceback:
I am facing a runtime error while loading the state_dict for actor model .I searched google but couldnt find similar issues .

RuntimeError: Error(s) in loading state_dict for Actor:
    Missing key(s) in state_dict: "layer_1.weight", "layer_1.bias", "layer_2.weight", "layer_2.bias", "layer_3.weight", "layer_3.bias". 
    Unexpected key(s) in state_dict: "encoder.0.weight", "encoder.0.bias", "encoder.2.weight", "encoder.2.bias", "encoder.2.running_mean", "encoder.2.running_var", "encoder.2.num_batches_tracked", "encoder.3.weight", "encoder.3.bias", "encoder.5.weight", "encoder.5.bias", "encoder.5.running_mean", "encoder.5.running_var", "encoder.5.num_batches_tracked", "encoder.6.weight", "encoder.6.bias", "encoder.8.weight", "encoder.8.bias", "encoder.8.running_mean", "encoder.8.running_var", "encoder.8.num_batches_tracked", "encoder.10.weight", "encoder.10.bias", "encoder.12.weight", "encoder.12.bias", "encoder.12.running_mean", "encoder.12.running_var", "encoder.12.num_batches_tracked", "encoder.13.weight", "encoder.13.bias", "encoder.15.weight", "encoder.15.bias", "encoder.15.running_mean", "encoder.15.running_var", "encoder.15.num_batches_tracked", "encoder.16.weight", "encoder.16.bias", "linear.0.weight", "linear.0.bias", "linear.2.weight", "linear.2.bias". 


it was answered by @MicaelJungo
The weights you saved were not from the model you are using here. Make sure to load the correct checkpoint, which was created when training this particular model.
