Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym by Nimish Sanghi

Author:Nimish Sanghi [Sanghi, Nimish]
Language: eng
Format: epub
ISBN: 9781484268094

def __len__(self):

return len(self.buffer)

def add(self, state, action, reward, next_state, done):

item = (state, action, reward, next_state, done)

max_priority = self.priorities.max()

if len(self.buffer) < self.size:

self.buffer.append(item)

else:

self.buffer[self.next_id] = item

self.priorities[self.next_id] = max_priority

self.next_id = (self.next_id + 1) % self.size

def sample(self, batch_size):

priorities = self.priorities[:len(self.buffer)]

probabilities = priorities ** self.alpha

probabilities /= probabilities.sum()

N = len(self.buffer)

weights = (N * probabilities) ** (-self.beta)

weights /= weights.max()

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