QLearning_PI.py

import numpy as np
import pandas as pd

'''
Initialize Q(s,a), all s in S, a in A(s), arbitrarily, and Q(terminal-state, .) = 0
Repeat (for each episode):
    Initialize S
    Repeat (for each step of episode):
        Choose A from S using policy derived from Q (e.g. epsilon-greedy)
        Take action A, observe R, S'
        Q(S, A) <- Q(S, A) + alpha * [R + gamma * max_a(Q(S',A)) - Q(S,A)]
        S <- S'
    until S is terminal
'''

class QLearning:

    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="Q-Learning"

    '''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


    '''Choose the next best action given the state'''
    def choose_best_action(self, observation):
        self.check_state_exist(observation)

        state_action = self.q_table.loc[observation, :]
        action = np.random.choice(state_action[state_action == np.max(state_action)].index)
        return action

    '''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_)
        if s_ != 'terminal':
            best_action = self.choose_best_action(str(s_))
            q_target = self.q_table.loc[s, a] + self.lr * (r + self.gamma * self.q_table.loc[s_, best_action] - 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
        a_ = self.choose_action(str(s_))
        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,
                )
            )