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mcts

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This commit is contained in:
Dong Yan 2017-11-16 17:05:54 +08:00
parent df57fdb411
commit 767fd4ea20
2 changed files with 71 additions and 31 deletions
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79
tianshou/core/mcts.py
View File

@ -1,12 +1,11 @@
import numpy as np
import math
import json
import time
js = json.load("state_mask.json")
action_num = 2
c_puct = 5.
class MCTSNode:
class MCTSNode(object):
def __init__(self, parent, action, state, action_num, prior):
self.parent = parent
self.action = action
@ -15,17 +14,17 @@ class MCTSNode:
self.action_num = action_num
self.prior = prior
def select_leaf(self):
raise NotImplementedError("Need to implement function select_leaf")
def selection(self):
raise NotImplementedError("Need to implement function selection")
def backup_value(self, action, value):
raise NotImplementedError("Need to implement function backup_value")
def backpropagation(self, action, value):
raise NotImplementedError("Need to implement function backpropagation")
def expand(self, action):
raise NotImplementedError("Need to implement function expand")
def expansion(self, simulator, action):
raise NotImplementedError("Need to implement function expansion")
def iteration(self):
raise NotImplementedError("Need to implement function iteration")
def simulation(self, state, evaluator):
raise NotImplementedError("Need to implement function simulation")
class UCTNode(MCTSNode):
@ -36,25 +35,31 @@ class UCTNode(MCTSNode):
self.N = np.zeros([action_num])
self.ucb = self.Q + c_puct * self.prior * math.sqrt(np.sum(self.N)) / (self.N + 1)
def select_leaf(self):
def selection(self):
action = np.argmax(self.ucb)
if action in self.children.keys():
self.children[action].select_leaf()
self.children[action].selection()
else:
# TODO: apply the action and evalate next state
# state, value = self.env.step_forward(self.state, action)
# self.children[action] = MCTSNode(self.env, self, action, state, prior)
# self.backup_value(action, value)
state, value = self.expand(action)
self.children[action] = UCTNode(self.env, self, action, state, prior)
return self, action
def backup_value(self, action, value):
def backpropagation(self, action, value):
self.N[action] += 1
self.W[action] += 1
self.Q = self.W / self.N
self.ucb = self.Q + c_puct * self.prior * math.sqrt(np.sum(self.N)) / (self.N + 1)
self.parent.backup_value(self.parent.action, value)
def expansion(self, simulator, action):
next_state = simulator.step_forward(self.state, action)
# TODO: Let users/evaluator give the prior
prior = np.ones([self.action_num]) / self.action_num
self.children[action] = UCTNode(self, action, next_state, self.action_num, prior)
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)
@ -65,9 +70,41 @@ class TSNode(MCTSNode):
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.value = {}
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
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):
self.expand()
def expand(self):
node, new_action = self.root.selection()
node.expansion(self.simulator, new_action)
value = node.simulation(self.evaluator, node.children[new_action].state)
node.backpropagation(new_action, value)
if __name__=="__main__":
pass

23
tianshou/core/mcts_test.py
View File

@ -1,25 +1,28 @@
import numpy as np
from mcts import MCTS
class TestEnv:
def __init__(self, max_step=5):
self.step = 0
self.state = 0
self.max_step = max_step
self.reward = {i:np.random.uniform() for i in range(2**max_step)}
self.best = max(self.reward.items(), key=lambda x:x[1])
print("The best arm is {} with expected reward {}".format(self.best[0],self.best[1]))
def step_forward(self, action):
print("Operate action {} at timestep {}".format(action, self.step))
self.state = self.state + 2**self.step*action
self.step = self.step + 1
if self.step == self.max_step:
reward = int(np.random.uniform() > self.reward[self.state])
def step_forward(self, state, action):
if action != 0 and action != 1:
raise ValueError("Action must be 0 or 1!")
if state[0] >= 2**state[1] or state[1] >= self.max_step:
raise ValueError("Invalid State!")
print("Operate action {} at state {}, timestep {}".format(action, state[0], state[1]))
state[0] = state[0] + 2**state[1]*action
state[1] = state[1] + 1
if state[1] == self.max_step:
reward = int(np.random.uniform() > self.reward[state[0]])
print("Get reward {}".format(reward))
else:
reward = 0
return [self.state, reward]
return [state, reward]
if __name__=="__main__":
env = TestEnv(1)
env.step_forward(1)
env.step_forward([0,0],1)