h1_task_button/h1taskbutton/rollout_storage.py

# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# Copyright (c) 2021 ETH Zurich, Nikita Rudin

import torch
import numpy as np

from rsl_rl.utils import split_and_pad_trajectories

class RolloutStorage:
    class Transition:
        def __init__(self):
            self.observations = None
            self.critic_observations = None
            self.actions = None
            self.rewards = None
            self.dones = None
            self.values = None
            self.actions_log_prob = None
            self.action_mean = None
            self.action_sigma = None
            self.hidden_states = None
        
        def clear(self):
            self.__init__()

    def __init__(self, num_envs, num_transitions_per_env, obs_shape, obs_vision_shape, privileged_obs_shape, actions_shape, device='cpu'):

        self.device = device

        self.obs_shape = obs_shape
        self.privileged_obs_shape = privileged_obs_shape
        self.actions_shape = actions_shape

        # Core
        self.observations = torch.zeros(num_transitions_per_env, num_envs, *obs_shape, device=self.device)
        self.observations_vision = torch.zeros(num_transitions_per_env, num_envs, *obs_vision_shape, device=self.device) if obs_vision_shape is not None else None
        if privileged_obs_shape[0] is not None:
            self.privileged_observations = torch.zeros(num_transitions_per_env, num_envs, *privileged_obs_shape, device=self.device)
        else:
            self.privileged_observations = None
        self.rewards = torch.zeros(num_transitions_per_env, num_envs, 1, device=self.device)
        self.actions = torch.zeros(num_transitions_per_env, num_envs, *actions_shape, device=self.device)
        self.dones = torch.zeros(num_transitions_per_env, num_envs, 1, device=self.device).byte()

        # For PPO
        self.actions_log_prob = torch.zeros(num_transitions_per_env, num_envs, 1, device=self.device)
        self.values = torch.zeros(num_transitions_per_env, num_envs, 1, device=self.device)
        self.returns = torch.zeros(num_transitions_per_env, num_envs, 1, device=self.device)
        self.advantages = torch.zeros(num_transitions_per_env, num_envs, 1, device=self.device)
        self.mu = torch.zeros(num_transitions_per_env, num_envs, *actions_shape, device=self.device)
        self.sigma = torch.zeros(num_transitions_per_env, num_envs, *actions_shape, device=self.device)

        self.num_transitions_per_env = num_transitions_per_env
        self.num_envs = num_envs

        # rnn
        self.saved_hidden_states_a = None
        self.saved_hidden_states_c = None

        self.step = 0

    def add_transitions(self, transition: Transition):
        if self.step >= self.num_transitions_per_env:
            raise AssertionError("Rollout buffer overflow")
    
        if isinstance(transition.observations, tuple) and self.observations_vision is not None:
            state_obs, vision_obs = transition.observations
            self.observations[self.step].copy_(state_obs)
            self.observations_vision[self.step].copy_(vision_obs)
        else:
            self.observations[self.step].copy_(transition.observations)
        if self.privileged_observations is not None: 
            if isinstance(transition.critic_observations, tuple):
                state_obs, vision_obs = transition.critic_observations
                self.privileged_observations[self.step].copy_(state_obs)
            else:
                self.privileged_observations[self.step].copy_(transition.critic_observations)
        self.actions[self.step].copy_(transition.actions)
        self.rewards[self.step].copy_(transition.rewards.view(-1, 1))
        self.dones[self.step].copy_(transition.dones.view(-1, 1))
        self.values[self.step].copy_(transition.values)
        self.actions_log_prob[self.step].copy_(transition.actions_log_prob.view(-1, 1))
        self.mu[self.step].copy_(transition.action_mean)
        self.sigma[self.step].copy_(transition.action_sigma)
        self._save_hidden_states(transition.hidden_states)
        self.step += 1

    def _save_hidden_states(self, hidden_states):
        if hidden_states is None or hidden_states==(None, None):
            return
        # make a tuple out of GRU hidden state sto match the LSTM format
        hid_a = hidden_states[0] if isinstance(hidden_states[0], tuple) else (hidden_states[0],)
        hid_c = hidden_states[1] if isinstance(hidden_states[1], tuple) else (hidden_states[1],)

        # initialize if needed 
        if self.saved_hidden_states_a is None:
            self.saved_hidden_states_a = [torch.zeros(self.observations.shape[0], *hid_a[i].shape, device=self.device) for i in range(len(hid_a))]
            self.saved_hidden_states_c = [torch.zeros(self.observations.shape[0], *hid_c[i].shape, device=self.device) for i in range(len(hid_c))]
        # copy the states
        for i in range(len(hid_a)):
            self.saved_hidden_states_a[i][self.step].copy_(hid_a[i])
            self.saved_hidden_states_c[i][self.step].copy_(hid_c[i])


    def clear(self):
        self.step = 0

    def compute_returns(self, last_values, gamma, lam):
        advantage = 0
        for step in reversed(range(self.num_transitions_per_env)):
            if step == self.num_transitions_per_env - 1:
                next_values = last_values
            else:
                next_values = self.values[step + 1]
            next_is_not_terminal = 1.0 - self.dones[step].float()
            delta = self.rewards[step] + next_is_not_terminal * gamma * next_values - self.values[step]
            advantage = delta + next_is_not_terminal * gamma * lam * advantage
            self.returns[step] = advantage + self.values[step]

        # Compute and normalize the advantages
        self.advantages = self.returns - self.values
        self.advantages = (self.advantages - self.advantages.mean()) / (self.advantages.std() + 1e-8)

    def get_statistics(self):
        done = self.dones
        done[-1] = 1
        flat_dones = done.permute(1, 0, 2).reshape(-1, 1)
        done_indices = torch.cat((flat_dones.new_tensor([-1], dtype=torch.int64), flat_dones.nonzero(as_tuple=False)[:, 0]))
        trajectory_lengths = (done_indices[1:] - done_indices[:-1])
        return trajectory_lengths.float().mean(), self.rewards.mean()

    def mini_batch_generator(self, num_mini_batches, num_epochs=5):
        batch_size = self.num_envs * self.num_transitions_per_env
        mini_batch_size = batch_size // num_mini_batches
        indices = torch.randperm(num_mini_batches*mini_batch_size, requires_grad=False, device=self.device)

        observations = self.observations.flatten(0, 1)
        if self.privileged_observations is not None:
            critic_observations = self.privileged_observations.flatten(0, 1)
        else:
            critic_observations = observations

        actions = self.actions.flatten(0, 1)
        values = self.values.flatten(0, 1)
        returns = self.returns.flatten(0, 1)
        old_actions_log_prob = self.actions_log_prob.flatten(0, 1)
        advantages = self.advantages.flatten(0, 1)
        old_mu = self.mu.flatten(0, 1)
        old_sigma = self.sigma.flatten(0, 1)

        for epoch in range(num_epochs):
            for i in range(num_mini_batches):

                start = i*mini_batch_size
                end = (i+1)*mini_batch_size
                batch_idx = indices[start:end]

                obs_batch = observations[batch_idx]
                critic_observations_batch = critic_observations[batch_idx]
                actions_batch = actions[batch_idx]
                target_values_batch = values[batch_idx]
                returns_batch = returns[batch_idx]
                old_actions_log_prob_batch = old_actions_log_prob[batch_idx]
                advantages_batch = advantages[batch_idx]
                old_mu_batch = old_mu[batch_idx]
                old_sigma_batch = old_sigma[batch_idx]
                if self.observations_vision is not None:
                    obs_vision_batch = self.observations_vision.flatten(0, 1)[batch_idx]
                    yield (obs_batch, obs_vision_batch), (critic_observations_batch, obs_vision_batch), actions_batch, target_values_batch, advantages_batch, returns_batch, \
                           old_actions_log_prob_batch, old_mu_batch, old_sigma_batch, (None, None),  None
                else:
                    yield obs_batch, critic_observations_batch, actions_batch, target_values_batch, advantages_batch, returns_batch, \
                           old_actions_log_prob_batch, old_mu_batch, old_sigma_batch, (None, None), None

    # for RNNs only
    def recurrent_mini_batch_generator(self, num_mini_batches, num_epochs=5):

        padded_obs_trajectories, trajectory_masks = split_and_pad_trajectories(self.observations, self.dones)
        if self.privileged_observations is not None: 
            padded_critic_obs_trajectories, _ = split_and_pad_trajectories(self.privileged_observations, self.dones)
        else: 
            padded_critic_obs_trajectories = padded_obs_trajectories

        mini_batch_size = self.num_envs // num_mini_batches
        for ep in range(num_epochs):
            first_traj = 0
            for i in range(num_mini_batches):
                start = i*mini_batch_size
                stop = (i+1)*mini_batch_size

                dones = self.dones.squeeze(-1)
                last_was_done = torch.zeros_like(dones, dtype=torch.bool)
                last_was_done[1:] = dones[:-1]
                last_was_done[0] = True
                trajectories_batch_size = torch.sum(last_was_done[:, start:stop])
                last_traj = first_traj + trajectories_batch_size
                
                masks_batch = trajectory_masks[:, first_traj:last_traj]
                obs_batch = padded_obs_trajectories[:, first_traj:last_traj]
                critic_obs_batch = padded_critic_obs_trajectories[:, first_traj:last_traj]

                actions_batch = self.actions[:, start:stop]
                old_mu_batch = self.mu[:, start:stop]
                old_sigma_batch = self.sigma[:, start:stop]
                returns_batch = self.returns[:, start:stop]
                advantages_batch = self.advantages[:, start:stop]
                values_batch = self.values[:, start:stop]
                old_actions_log_prob_batch = self.actions_log_prob[:, start:stop]

                # reshape to [num_envs, time, num layers, hidden dim] (original shape: [time, num_layers, num_envs, hidden_dim])
                # then take only time steps after dones (flattens num envs and time dimensions),
                # take a batch of trajectories and finally reshape back to [num_layers, batch, hidden_dim]
                last_was_done = last_was_done.permute(1, 0)
                hid_a_batch = [ saved_hidden_states.permute(2, 0, 1, 3)[last_was_done][first_traj:last_traj].transpose(1, 0).contiguous()
                                for saved_hidden_states in self.saved_hidden_states_a ] 
                hid_c_batch = [ saved_hidden_states.permute(2, 0, 1, 3)[last_was_done][first_traj:last_traj].transpose(1, 0).contiguous()
                                for saved_hidden_states in self.saved_hidden_states_c ]
                # remove the tuple for GRU
                hid_a_batch = hid_a_batch[0] if len(hid_a_batch)==1 else hid_a_batch
                hid_c_batch = hid_c_batch[0] if len(hid_c_batch)==1 else hid_a_batch

                yield obs_batch, critic_obs_batch, actions_batch, values_batch, advantages_batch, returns_batch, \
                       old_actions_log_prob_batch, old_mu_batch, old_sigma_batch, (hid_a_batch, hid_c_batch), masks_batch
                
                first_traj = last_traj