human
I am working on the file "16_reinforcement_learning.ipynb".
The first task is:
Note: in the first version of the book, the loss function was simply the squared error between the target Q-Values (y) and the estimated Q-Values (q_value). However, because the experiences are very noisy, it is better to use a quadratic loss only for small errors (below 1.0) and a linear loss (twice the absolute error) for larger errors, which is what the code above computes. This way large errors don't push the model parameters around as much. Note that we also tweaked some hyperparameters (using a smaller learning rate, and using Nesterov Accelerated Gradients rather than Adam optimization, since adaptive gradient algorithms may sometimes be bad, according to this paper). We also tweaked a few other hyperparameters below (a larger replay memory, longer decay for the $\epsilon$-greedy policy, larger discount rate, less frequent copies of the online DQN to the target DQN, etc.).
We use this ReplayMemory class instead of a deque because it is much faster for random access (thanks to @NileshPS who contributed it). Moreover, we default to sampling with replacement, which is much faster than sampling without replacement for large replay memories.
Can you write Python code for it?
assistant
#Here the code above computes.

class ReplayMemory:
    def __init__(self, maxlen):
        self.maxlen = maxlen
        self.buf = np.empty(shape=maxlen, dtype=np.object)
        self.index = 0
        self.length = 0
        
    def append(self, data):
        self.buf[self.index] = data
        self.length = min(self.length + 1, self.maxlen)
        self.index = (self.index + 1) % self.maxlen
    
    def sample(self, batch_size, with_replacement=True):
        if with_replacement:
            indices = np.random.randint(self.length, size=batch_size) # faster
        else:
            indices = np.random.permutation(self.length)[:batch_size]
        return self.buf[indices]

replay_memory_size = 500000
replay_memory = ReplayMemory(replay_memory_size)

def sample_memories(batch_size):
    cols = [[], [], [], [], []] # state, action, reward, next_state, continue
    for memory in replay_memory.sample(batch_size):
        for col, value in zip(cols, memory):
            col.append(value)
    cols = [np.array(col) for col in cols]
    return cols[0], cols[1], cols[2].reshape(-1, 1), cols[3], cols[4].reshape(-1, 1)

eps_min = 0.1
eps_max = 1.0
eps_decay_steps = 2000000

def epsilon_greedy(q_values, step):
    epsilon = max(eps_min, eps_max - (eps_max-eps_min) * step/eps_decay_steps)
    if np.random.rand() < epsilon:
        return np.random.randint(n_outputs) # random action
    else:
        return np.argmax(q_values) # optimal action

n_steps = 4000000  # total number of training steps
training_start = 10000  # start training after 10,000 game iterations
training_interval = 4  # run a training step every 4 game iterations
save_steps = 1000  # save the model every 1,000 training steps
copy_steps = 10000  # copy online DQN to target DQN every 10,000 training steps
discount_rate = 0.99
skip_start = 90  # Skip the start of every game (it's just waiting time).
batch_size = 50
iteration = 0  # game iterations
checkpoint_path = "./my_dqn.ckpt"
done = True # env needs to be reset