《用Python动手学习强化学习》【贝尔曼方程】【价值最大化】
代码取自：《用Python动手学强化学习》第二章，基于价值最大化的贝尔曼方程def R(s):
if s == "happy_end":
return 1
elif s == "bad_end":
return -1
else:
return 0
def transit_func(s, a):
actions = s.split("_")
LIMIT_GAME_COUNT = 5
HAPPY_END_BORDER = 4
MOVE_PROB = 0.9
def next_state(state, action):
return "_".join()
if len(actions) == LIMIT_GAME_COUNT:
up_count = sum()
state = "happy_end" if up_count >= HAPPY_END_BORDER else "bad_end"
prob = 1
return {state : prob}
else:#递归
opposite = "up" if a == "down" else "down"
return {next_state(s, a): MOVE_PROB,
next_state(s, opposite): 1 - MOVE_PROB}
def max_V_on_next_state(s):
if s in ["happy_end", "bad_end"]:
return 0
actions = ["up", "down"]
values = []
for a in actions:
transition_probs = transit_func(s, a)
v = 0
for next_state in transition_probs:
prob = transition_probs
v += prob * V(next_state)
values.append(v)
return max(values) {:10_327:}
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