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replay_buffer.py
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import torch
import numpy as np
from collections import deque
from sum_tree import SumTree
class ReplayBuffer(object):
def __init__(self, args):
self.batch_size = args.batch_size
self.buffer_capacity = args.buffer_capacity
self.current_size = 0
self.count = 0
self.buffer = {'state': np.zeros((self.buffer_capacity, args.state_dim)),
'action': np.zeros((self.buffer_capacity, 1)),
'reward': np.zeros(self.buffer_capacity),
'next_state': np.zeros((self.buffer_capacity, args.state_dim)),
'terminal': np.zeros(self.buffer_capacity),
}
def store_transition(self, state, action, reward, next_state, terminal, done):
self.buffer['state'][self.count] = state
self.buffer['action'][self.count] = action
self.buffer['reward'][self.count] = reward
self.buffer['next_state'][self.count] = next_state
self.buffer['terminal'][self.count] = terminal
self.count = (self.count + 1) % self.buffer_capacity # When the 'count' reaches buffer_capacity, it will be reset to 0.
self.current_size = min(self.current_size + 1, self.buffer_capacity)
def sample(self, total_steps):
index = np.random.randint(0, self.current_size, size=self.batch_size)
batch = {}
for key in self.buffer.keys(): # numpy->tensor
if key == 'action':
batch[key] = torch.tensor(self.buffer[key][index], dtype=torch.long)
else:
batch[key] = torch.tensor(self.buffer[key][index], dtype=torch.float32)
return batch, None, None
class N_Steps_ReplayBuffer(object):
def __init__(self, args):
self.gamma = args.gamma
self.batch_size = args.batch_size
self.buffer_capacity = args.buffer_capacity
self.current_size = 0
self.count = 0
self.n_steps = args.n_steps
self.n_steps_deque = deque(maxlen=self.n_steps)
self.buffer = {'state': np.zeros((self.buffer_capacity, args.state_dim)),
'action': np.zeros((self.buffer_capacity, 1)),
'reward': np.zeros(self.buffer_capacity),
'next_state': np.zeros((self.buffer_capacity, args.state_dim)),
'terminal': np.zeros(self.buffer_capacity),
}
def store_transition(self, state, action, reward, next_state, terminal, done):
transition = (state, action, reward, next_state, terminal, done)
self.n_steps_deque.append(transition)
if len(self.n_steps_deque) == self.n_steps:
state, action, n_steps_reward, next_state, terminal = self.get_n_steps_transition()
self.buffer['state'][self.count] = state
self.buffer['action'][self.count] = action
self.buffer['reward'][self.count] = n_steps_reward
self.buffer['next_state'][self.count] = next_state
self.buffer['terminal'][self.count] = terminal
self.count = (self.count + 1) % self.buffer_capacity # When the 'count' reaches buffer_capacity, it will be reset to 0.
self.current_size = min(self.current_size + 1, self.buffer_capacity)
def get_n_steps_transition(self):
state, action = self.n_steps_deque[0][:2]
next_state, terminal = self.n_steps_deque[-1][3:5]
n_steps_reward = 0
for i in reversed(range(self.n_steps)):
r, s_, ter, d = self.n_steps_deque[i][2:]
n_steps_reward = r + self.gamma * (1 - d) * n_steps_reward
if d:
next_state, terminal = s_, ter
return state, action, n_steps_reward, next_state, terminal
def sample(self, total_steps):
index = np.random.randint(0, self.current_size, size=self.batch_size)
batch = {}
for key in self.buffer.keys(): # numpy->tensor
if key == 'action':
batch[key] = torch.tensor(self.buffer[key][index], dtype=torch.long)
else:
batch[key] = torch.tensor(self.buffer[key][index], dtype=torch.float32)
return batch, None, None
class Prioritized_ReplayBuffer(object):
def __init__(self, args):
self.max_train_steps = args.max_train_steps
self.alpha = args.alpha
self.beta_init = args.beta_init
self.beta = args.beta_init
self.batch_size = args.batch_size
self.buffer_capacity = args.buffer_capacity
self.sum_tree = SumTree(self.buffer_capacity)
self.current_size = 0
self.count = 0
self.buffer = {'state': np.zeros((self.buffer_capacity, args.state_dim)),
'action': np.zeros((self.buffer_capacity, 1)),
'reward': np.zeros(self.buffer_capacity),
'next_state': np.zeros((self.buffer_capacity, args.state_dim)),
'terminal': np.zeros(self.buffer_capacity),
}
def store_transition(self, state, action, reward, next_state, terminal, done):
self.buffer['state'][self.count] = state
self.buffer['action'][self.count] = action
self.buffer['reward'][self.count] = reward
self.buffer['next_state'][self.count] = next_state
self.buffer['terminal'][self.count] = terminal
# 如果是第一条经验，初始化优先级为1.0；否则，对于新存入的经验，指定为当前最大的优先级
priority = 1.0 if self.current_size == 0 else self.sum_tree.priority_max
self.sum_tree.update(data_index=self.count, priority=priority) # 更新当前经验在sum_tree中的优先级
self.count = (self.count + 1) % self.buffer_capacity # When the 'count' reaches buffer_capacity, it will be reset to 0.
self.current_size = min(self.current_size + 1, self.buffer_capacity)
def sample(self, total_steps):
batch_index, IS_weight = self.sum_tree.get_batch_index(current_size=self.current_size, batch_size=self.batch_size, beta=self.beta)
self.beta = self.beta_init + (1 - self.beta_init) * (total_steps / self.max_train_steps) # beta：beta_init->1.0
batch = {}
for key in self.buffer.keys(): # numpy->tensor
if key == 'action':
batch[key] = torch.tensor(self.buffer[key][batch_index], dtype=torch.long)
else:
batch[key] = torch.tensor(self.buffer[key][batch_index], dtype=torch.float32)
return batch, batch_index, IS_weight
def update_batch_priorities(self, batch_index, td_errors): # 根据传入的td_error，更新batch_index所对应数据的priorities
priorities = (np.abs(td_errors) + 0.01) ** self.alpha
for index, priority in zip(batch_index, priorities):
self.sum_tree.update(data_index=index, priority=priority)
class N_Steps_Prioritized_ReplayBuffer(object):
def __init__(self, args):
self.max_train_steps = args.max_train_steps
self.alpha = args.alpha
self.beta_init = args.beta_init
self.beta = args.beta_init
self.gamma = args.gamma
self.batch_size = args.batch_size
self.buffer_capacity = args.buffer_capacity
self.sum_tree = SumTree(self.buffer_capacity)
self.n_steps = args.n_steps
self.n_steps_deque = deque(maxlen=self.n_steps)
self.buffer = {'state': np.zeros((self.buffer_capacity, args.state_dim)),
'action': np.zeros((self.buffer_capacity, 1)),
'reward': np.zeros(self.buffer_capacity),
'next_state': np.zeros((self.buffer_capacity, args.state_dim)),
'terminal': np.zeros(self.buffer_capacity),
}
self.current_size = 0
self.count = 0
def store_transition(self, state, action, reward, next_state, terminal, done):
transition = (state, action, reward, next_state, terminal, done)
self.n_steps_deque.append(transition)
if len(self.n_steps_deque) == self.n_steps:
state, action, n_steps_reward, next_state, terminal = self.get_n_steps_transition()
self.buffer['state'][self.count] = state
self.buffer['action'][self.count] = action
self.buffer['reward'][self.count] = n_steps_reward
self.buffer['next_state'][self.count] = next_state
self.buffer['terminal'][self.count] = terminal
# 如果是buffer中的第一条经验，那么指定priority为1.0；否则对于新存入的经验，指定为当前最大的priority
priority = 1.0 if self.current_size == 0 else self.sum_tree.priority_max
self.sum_tree.update(data_index=self.count, priority=priority) # 更新当前经验在sum_tree中的优先级
self.count = (self.count + 1) % self.buffer_capacity # When 'count' reaches buffer_capacity, it will be reset to 0.
self.current_size = min(self.current_size + 1, self.buffer_capacity)
def sample(self, total_steps):
batch_index, IS_weight = self.sum_tree.get_batch_index(current_size=self.current_size, batch_size=self.batch_size, beta=self.beta)
self.beta = self.beta_init + (1 - self.beta_init) * (total_steps / self.max_train_steps) # beta：beta_init->1.0
batch = {}
for key in self.buffer.keys(): # numpy->tensor
if key == 'action':
batch[key] = torch.tensor(self.buffer[key][batch_index], dtype=torch.long)
else:
batch[key] = torch.tensor(self.buffer[key][batch_index], dtype=torch.float32)
return batch, batch_index, IS_weight
def get_n_steps_transition(self):
state, action = self.n_steps_deque[0][:2] # 获取deque中第一个transition的s和a
next_state, terminal = self.n_steps_deque[-1][3:5] # 获取deque中最后一个transition的s'和terminal
n_steps_reward = 0
for i in reversed(range(self.n_steps)): # 逆序计算n_steps_reward
r, s_, ter, d = self.n_steps_deque[i][2:]
n_steps_reward = r + self.gamma * (1 - d) * n_steps_reward
if d: # 如果done=True，说明一个回合结束，保存deque中当前这个transition的s'和terminal作为这个n_steps_transition的next_state和terminal
next_state, terminal = s_, ter
return state, action, n_steps_reward, next_state, terminal
def update_batch_priorities(self, batch_index, td_errors): # 根据传入的td_error，更新batch_index所对应数据的priorities
priorities = (np.abs(td_errors) + 0.01) ** self.alpha
for index, priority in zip(batch_index, priorities):
self.sum_tree.update(data_index=index, priority=priority)