utils.py

import logging
import json
import copy
import random
from collections import namedtuple, deque, defaultdict
import numpy as np
from visualiser import VisdomWriter
# Pytorch
import torch

logger = logging.getLogger(__name__)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger.debug('Device Info:{}'.format(device))

def save_to_json(dict_item, fname):
    with open(fname, 'w') as f:
        json.dump(dict_item, f)
    
def save_to_txt(item, fname):
    with open(fname, 'a') as f:
        f.write('{}\n'.format(item))

def flatten(tensor):
    return torch.reshape(tensor, (tensor.shape[0], -1,))

class VisWriter:
    """Dummy Visdom Writer"""
    def __init__(self, vis=True):
        self.vis = vis
        if self.vis:
            self.writer = VisdomWriter(enabled=True, logger=logger)        

    def text(self, message, title):
        if self.vis:
            self.writer.text(message, title)

    def push(self, item, title):
        if self.vis:
            self.writer.push(item, title)

class OUNoise:
    """Ornstein-Uhlenbeck process."""
    def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
        """Initialize parameters and noise process."""
        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."""
        x = self.state
        dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])
        self.state = (x + dx)/2
        return self.state

class ReplayBuffer:
    """Fixed-size buffer to store experience tuples."""

    def __init__(self, action_size, buffer_size, batch_size, seed):
        """Initialize a ReplayBuffer object.
        Params
        ======
            buffer_size (int): maximum size of buffer
            batch_size (int): size of each training batch
        """
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)  # internal memory (deque)
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
        self.seed = random.seed(seed)
    
    def add(self, state, action, reward, next_state, done):
        """Add a new experience to memory."""
        e = self.experience(state, action, reward, next_state, done)
        self.memory.append(e)
    
    def sample(self):
        """Randomly sample a batch of experiences from memory."""
        experiences = random.sample(self.memory, k=self.batch_size)

        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)
        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)

        return (states, actions, rewards, next_states, dones)

    def __len__(self):
        """Return the current size of internal memory."""
        return len(self.memory)