rl.py

"""
Created on Tue April 24 21:29 2018

@author: hanxy
"""

import numpy as np
import pandas as pd


# Base Reinforcement Learning
class RL(object):

    def __init__(self, actions,
                 learning_rate=0.01,
                 reward_decay=0.9,
                 e_greedy=0.9):
        self.actions = actions
        self.n_actions = len(actions)
        self.LR = learning_rate
        self.Gamma = reward_decay
        self.Epsilon = e_greedy
        self.q_table = pd.DataFrame(columns=self.actions,
                                    dtype=np.float64)

    # choose action based on observation
    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.loc[observation, :]
            state_action = state_action.reindex(
                np.random.permutation(state_action.index
                                      ))
            action = state_action.idxmax()
        else:
            # choose random action
            action = np.random.choice(self.actions)
        return action

    def learn(self, *args):
        pass

    # check if state exists
    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(
                    np.zeros(self.n_actions),
                    index=self.q_table.columns,
                    name=state
                )
            )


# Q-learning
class QLearning(RL):
    def __init__(self, actions,
                 learning_rate=0.01,
                 reward_decay=0.9,
                 e_greedy=0.9):
        super(QLearning, self).__init__(actions,
                                        learning_rate,
                                        reward_decay,
                                        e_greedy)

    def learn(self, s, a, r, s_):
        self.check_state_exist(s_)
        q_predict = self.q_table.loc[s, a]
        if s_ != 'terminal' and s_ != 'hell':
            q_target = r + self.Gamma * self.q_table.loc[s_, :].max()
        else:
            q_target = r
        self.q_table.loc[s, a] += self.LR * (q_target - q_predict)


# Sarsa on-policy
class Sarsa(RL):
    def __init__(self, actions,
                 learning_rate=0.01,
                 reward_decay=0.9,
                 e_greedy=0.9):
        super(Sarsa, self).__init__(actions,
                                    learning_rate,
                                    reward_decay,
                                    e_greedy)

    def learn(self, s, a, r, s_, a_):
        self.check_state_exist(s_)
        q_predict = self.q_table.loc[s, a]
        if s_ != 'terminal' and s_ != 'hell':
            q_target = r + self.Gamma * self.q_table.loc[s_, a_]
        else:
            q_target = r
        self.q_table.loc[s, a] += self.LR * (q_target - q_predict)


# backward eligibility traces
class SarsaLambda(RL):
    def __init__(self, actions,
                 learning_rate=0.01,
                 reward_decay=0.9,
                 e_greedy=0.9,
                 trace_decay=0.9):
        super(SarsaLambda, self).__init__(actions,
                                          learning_rate,
                                          reward_decay,
                                          e_greedy)
        self.Lambda = trace_decay
        self.eligibility_trace = self.q_table

    def check_state_exist(self, state):
        if state not in self.q_table.index:
            # append new state to q table
            to_be_appended = pd.Series(
                np.zeros(self.n_actions),
                index=self.q_table.columns,
                name=state
            )
            self.q_table = self.q_table.append(to_be_appended)

            # also update eligibility trace
            self.eligibility_trace = self.eligibility_trace.append(to_be_appended)

    def learn(self, s, a, r, s_, a_):
        self.check_state_exist(s_)
        q_predict = self.q_table.loc[s, a]
        if s_ != 'terminal' and s_ != 'hell':
            q_target = r + self.Gamma * self.q_table.loc[s_, a_]
        else:
            q_target = r
        error = q_target - q_predict

        # increase trace amount for visited state-action pair

        # Method 1:
        # self.eligibility_trace.loc[s, a] += 1

        # Method 2:
        self.eligibility_trace.loc[s, :] *= 0
        self.eligibility_trace.loc[s, a] = 1

        # Q update
        self.q_table += self.LR * error * self.eligibility_trace

        # decay eligibility trace after update
        self.eligibility_trace *= self.Gamma * self.Lambda