| import numpy as np | |
| import torch | |
| class ReplayMemory(): | |
| """Buffer to store environment transitions.""" | |
| def __init__(self, state_dim, action_dim, capacity, device): | |
| self.capacity = int(capacity) | |
| self.device = device | |
| self.states = np.empty((self.capacity, int(state_dim)), dtype=np.float32) | |
| self.actions = np.empty((self.capacity, int(action_dim)), dtype=np.float32) | |
| self.rewards = np.empty((self.capacity, 1), dtype=np.float32) | |
| self.next_states = np.empty((self.capacity, int(state_dim)), dtype=np.float32) | |
| self.masks = np.empty((self.capacity, 1), dtype=np.float32) | |
| self.idx = 0 | |
| self.full = False | |
| def append(self, state, action, reward, next_state, mask): | |
| np.copyto(self.states[self.idx], state) | |
| np.copyto(self.actions[self.idx], action) | |
| np.copyto(self.rewards[self.idx], reward) | |
| np.copyto(self.next_states[self.idx], next_state) | |
| np.copyto(self.masks[self.idx], mask) | |
| self.idx = (self.idx + 1) % self.capacity | |
| self.full = self.full or self.idx == 0 | |
| def sample(self, batch_size): | |
| idxs = np.random.randint( | |
| 0, self.capacity if self.full else self.idx, size=batch_size | |
| ) | |
| states = torch.as_tensor(self.states[idxs], device=self.device) | |
| actions = torch.as_tensor(self.actions[idxs], device=self.device) | |
| rewards = torch.as_tensor(self.rewards[idxs], device=self.device) | |
| next_states = torch.as_tensor(self.next_states[idxs], device=self.device) | |
| masks = torch.as_tensor(self.masks[idxs], device=self.device) | |
| return states, actions, rewards, next_states, masks | |
| class DiffusionMemory(): | |
| """Buffer to store best actions.""" | |
| def __init__(self, state_dim, action_dim, capacity, device): | |
| self.capacity = int(capacity) | |
| self.device = device | |
| self.states = np.empty((self.capacity, int(state_dim)), dtype=np.float32) | |
| self.best_actions = np.empty((self.capacity, int(action_dim)), dtype=np.float32) | |
| self.idx = 0 | |
| self.full = False | |
| def append(self, state, action): | |
| np.copyto(self.states[self.idx], state) | |
| np.copyto(self.best_actions[self.idx], action) | |
| self.idx = (self.idx + 1) % self.capacity | |
| self.full = self.full or self.idx == 0 | |
| def sample(self, batch_size): | |
| idxs = np.random.randint( | |
| 0, self.capacity if self.full else self.idx, size=batch_size | |
| ) | |
| states = torch.as_tensor(self.states[idxs], device=self.device) | |
| best_actions = torch.as_tensor(self.best_actions[idxs], device=self.device) | |
| best_actions.requires_grad_(True) | |
| return states, best_actions, idxs | |
| def replace(self, idxs, best_actions): | |
| np.copyto(self.best_actions[idxs], best_actions) | |