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RL_brain.py
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"""
This part of code is the Dyna-Q learning brain, which is a brain of the agent.
All decisions and learning processes are made in here.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
"""
import numpy as np
import pandas as pd
class QLearningTable:
def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):
self.actions = actions # a list
self.lr = learning_rate
self.gamma = reward_decay
self.epsilon = e_greedy
## argmax type error
self.q_table = pd.DataFrame(columns=self.actions).astype('float32')
def choose_action(self, observation):
self.check_state_exist(observation)
# action selection
if np.random.uniform() < self.epsilon:
# choose best action
# state_action = self.q_table.ix[observation, :]
state_action = self.q_table.loc[observation, :] # for label indexing
state_action = state_action.reindex(np.random.permutation(state_action.index)) # some actions have same value
action = state_action.argmax()
else:
# choose random action
action = np.random.choice(self.actions)
return action
def learn(self, s, a, r, s_):
self.check_state_exist(s_)
q_predict = self.q_table.loc[s, a]
if s_ != 'terminal':
q_target = r + self.gamma * self.q_table.loc[s_, :].max() # next state is not terminal
else:
q_target = r # next state is terminal
self.q_table.loc[s, a] += self.lr * (q_target - q_predict) # update
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,
)
)
class EnvModel:
"""Similar to the memory buffer in DQN, you can store past experiences in here.
Alternatively, the model can generate next state and reward signal accurately."""
def __init__(self, actions):
# the simplest case is to think about the model is a memory which has all past transition information
self.actions = actions
self.database = pd.DataFrame(columns=actions, dtype=np.object)
def store_transition(self, s, a, r, s_):
if s not in self.database.index:
self.database = self.database.append(
pd.Series(
[None] * len(self.actions),
index=self.database.columns,
name=s,
))
self.database.set_value(s, a, (r, s_))
def sample_s_a(self):
s = np.random.choice(self.database.index)
a = np.random.choice(self.database.loc[s].dropna().index) # filter out the None value
return s, a
def get_r_s_(self, s, a):
r, s_ = self.database.loc[s, a]
return r, s_