142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
import numpy as np
|
|
import math
|
|
import time
|
|
|
|
c_puct = 5
|
|
|
|
|
|
class MCTSNode(object):
|
|
def __init__(self, parent, action, state, action_num, prior):
|
|
self.parent = parent
|
|
self.action = action
|
|
self.children = {}
|
|
self.state = state
|
|
self.action_num = action_num
|
|
self.prior = prior
|
|
|
|
def selection(self, simulator):
|
|
raise NotImplementedError("Need to implement function selection")
|
|
|
|
def backpropagation(self, action):
|
|
raise NotImplementedError("Need to implement function backpropagation")
|
|
|
|
def simulation(self, state, evaluator):
|
|
raise NotImplementedError("Need to implement function simulation")
|
|
|
|
|
|
class UCTNode(MCTSNode):
|
|
def __init__(self, parent, action, state, action_num, prior):
|
|
super(UCTNode, self).__init__(parent, action, state, action_num, prior)
|
|
self.Q = np.zeros([action_num])
|
|
self.W = np.zeros([action_num])
|
|
self.N = np.zeros([action_num])
|
|
self.ucb = self.Q + c_puct * self.prior * math.sqrt(np.sum(self.N)) / (self.N + 1)
|
|
|
|
def selection(self, simulator):
|
|
action = np.argmax(self.ucb)
|
|
if action in self.children.keys():
|
|
return self.children[action].selection(simulator)
|
|
else:
|
|
self.children[action] = ActionNode(self, action)
|
|
return self.children[action].selection(simulator)
|
|
|
|
def backpropagation(self, action):
|
|
action = int(action)
|
|
self.N[action] += 1
|
|
self.W[action] += self.children[action].reward
|
|
for i in range(self.action_num):
|
|
if self.N[i] != 0:
|
|
self.Q[i] = (self.W[i] + 0.) / self.N[i]
|
|
self.ucb = self.Q + c_puct * self.prior * math.sqrt(np.sum(self.N)) / (self.N + 1.)
|
|
if self.parent is not None:
|
|
self.parent.backpropagation(self.children[action].reward)
|
|
|
|
def simulation(self, evaluator, state):
|
|
value = evaluator(state)
|
|
return value
|
|
|
|
|
|
class TSNode(MCTSNode):
|
|
def __init__(self, parent, action, state, action_num, prior, method="Gaussian"):
|
|
super(TSNode, self).__init__(parent, action, state, action_num, prior)
|
|
if method == "Beta":
|
|
self.alpha = np.ones([action_num])
|
|
self.beta = np.ones([action_num])
|
|
if method == "Gaussian":
|
|
self.mu = np.zeros([action_num])
|
|
self.sigma = np.zeros([action_num])
|
|
|
|
|
|
class ActionNode:
|
|
def __init__(self, parent, action):
|
|
self.parent = parent
|
|
self.action = action
|
|
self.children = {}
|
|
self.next_state = None
|
|
self.reward = 0
|
|
|
|
def selection(self, simulator):
|
|
self.next_state, self.reward = simulator.step_forward(self.parent.state, self.action)
|
|
if self.next_state is not None:
|
|
if self.next_state in self.children.keys():
|
|
return self.children[self.next_state].selection(simulator)
|
|
else:
|
|
return self.parent, self.action
|
|
else:
|
|
return self.parent, self.action
|
|
|
|
def expansion(self, action_num):
|
|
# TODO: Let users/evaluator give the prior
|
|
if self.next_state is not None:
|
|
prior = np.ones([action_num]) / action_num
|
|
self.children[self.next_state] = UCTNode(self, self.action, self.next_state, action_num, prior)
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def backpropagation(self, value):
|
|
self.reward += value
|
|
self.parent.backpropagation(self.action)
|
|
|
|
|
|
class MCTS:
|
|
def __init__(self, simulator, evaluator, root, action_num, prior, method="UCT", max_step=None, max_time=None):
|
|
self.simulator = simulator
|
|
self.evaluator = evaluator
|
|
self.action_num = action_num
|
|
if method == "UCT":
|
|
self.root = UCTNode(None, None, root, action_num, prior)
|
|
if method == "TS":
|
|
self.root = TSNode(None, None, root, action_num, prior)
|
|
if max_step is not None:
|
|
self.step = 0
|
|
self.max_step = max_step
|
|
if max_time is not None:
|
|
self.start_time = time.time()
|
|
self.max_time = max_time
|
|
if max_step is None and max_time is None:
|
|
raise ValueError("Need a stop criteria!")
|
|
while (max_step is not None and self.step < self.max_step or max_step is None) \
|
|
and (max_time is not None and time.time() - self.start_time < self.max_time or max_time is None):
|
|
print("Q={}".format(self.root.Q))
|
|
print("N={}".format(self.root.N))
|
|
print("W={}".format(self.root.W))
|
|
print("UCB={}".format(self.root.ucb))
|
|
print("\n")
|
|
self.expand()
|
|
if max_step is not None:
|
|
self.step += 1
|
|
|
|
def expand(self):
|
|
node, new_action = self.root.selection(self.simulator)
|
|
success = node.children[new_action].expansion(self.action_num)
|
|
if success:
|
|
value = node.simulation(self.evaluator, node.children[new_action].next_state)
|
|
node.children[new_action].backpropagation(value + 0.)
|
|
else:
|
|
node.children[new_action].backpropagation(0.)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
pass
|