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RL_brain_sarsa_lambda.py
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RL_brain_sarsa_lambda.py
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import numpy as np
import pandas as pd
class rlalgorithm:
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.1, lambda_decay=0.9):
self.actions = actions
self.lr = learning_rate
self.gamma = reward_decay
self.epsilon = e_greedy
self.lambda_decay = lambda_decay
self.q_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
self.e_table = pd.DataFrame(columns=self.actions, dtype=np.float64)
self.display_name="Sarsa(λ)"
print("Using Sarsa(λ) ...")
'''Choose the next action to take given the observed state using an epsilon greedy policy'''
def choose_action(self, observation):
# Add non-existing state to our q_table
self.check_state_exist(observation)
# Select next action
if np.random.uniform() >= self.epsilon:
# Choose argmax action
state_action_values = self.q_table.loc[observation, :]
action = np.random.choice(state_action_values[state_action_values == np.max(state_action_values)].index) # handle multiple argmax with random
else:
# Choose random action
action = np.random.choice(self.actions)
return action
def learn(self, s, a, r, s_):
self.check_state_exist(s_)
# determine q_target
if s_ != 'terminal':
a_ = self.choose_action(str(s_)) # argmax action
q_target = r + self.gamma * self.q_table.loc[s_, a_] # max state-action value
else:
q_target = r # next state is terminal
# update q_table using eligibility trace
error = q_target - self.q_table.loc[s, a]
self.e_table.loc[s, a] += 1
self.q_table += self.lr * error * self.e_table # update state-action value for all states and actions
# update eligibility trace
if s_ != 'terminal':
self.e_table *= self.gamma * self.lambda_decay # decay the eligibility trace for all states and actions
else:
self.e_table = pd.DataFrame(columns=self.actions, dtype=np.float64) # clear the eligibility
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,
)
)
if state not in self.e_table.index:
# append new state to q table
self.e_table = self.e_table.append(
pd.Series(
[0]*len(self.actions),
index=self.e_table.columns,
name=state,
)
)