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ExpectedSARSA_PI.py
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ExpectedSARSA_PI.py
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import numpy as np
import pandas as pd
'''
Initialize Q(s,a), all s in S, a in A(s), arbitrarily, and Q(termina-state, .) = 0
Repeat (for each episode):
Initialize S
Choose A from S using policy derived from Q (e.g. epsilon-greedy)
Repeat (for each step of episode):
Take action A, observe R, S'
Q(S, A) <- Q(S, A) + alpha * [R + gamma * sum_a(policy(a|S')*Q(S',a)) - Q(S,A)]
S <- S', A <- A'
until S is terminal
'''
class ExpectedSarsa:
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.1):
self.actions = actions
self.lr = learning_rate
self.gamma = reward_decay
self.epsilon = e_greedy
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
self.display_name="ExpectedSARSA"
'''Choose the next action to take given the observed state using an epsilon greedy policy'''
def choose_action(self, observation):
self.check_state_exist(observation)
#BUG: Epsilon should be .1 and signify the small probability of NOT choosing max action
if np.random.uniform() >= self.epsilon:
state_action = self.q_table.loc[observation, :]
action = np.random.choice(state_action[state_action == np.max(state_action)].index)
else:
action = np.random.choice(self.actions)
return action
def sum_weighted_actions_q(self, observation):
self.check_state_exist(observation)
state_action = self.q_table.loc[observation, :]
best_action = np.random.choice(state_action[state_action == np.max(state_action)].index)
sum = 0
for action in self.actions:
# for each action
if action == best_action:
policy = 1-self.epsilon
else:
policy = self.epsilon/(len(self.actions) - 1)
sum += policy * self.q_table.loc[observation, action]
return sum
'''Update the Q(S,A) state-action value table using the latest experience
This is a not a very good learning update
'''
def learn(self, s, a, r, s_):
self.check_state_exist(s_)
a_ = self.choose_action(str(s_))
if s_ != 'terminal':
q_target = self.q_table.loc[s, a] + self.lr * (r + self.gamma * self.sum_weighted_actions_q(str(s_)) - self.q_table.loc[s, a]);
else:
q_target = self.q_table.loc[s, a] + self.lr * (r + self.q_table.loc[s, a]) # next state is terminal
self.q_table.loc[s, a] = q_target # update
return s_, a_
'''States are dynamically added to the Q(S,A) table as they are encountered'''
def check_state_exist(self, state):
if state not in self.q_table.index:
# append new state to q table
self.q_table = self.q_table.append(
pd.Series(
[0]*len(self.actions),
index=self.q_table.columns,
name=state,
)
)