rl.py

"""Reinforcement Learning (Chapter 21)"""

from collections import defaultdict
from utils import argmax
from mdp import MDP, policy_evaluation

import random


class PassiveADPAgent:

    """Passive (non-learning) agent that uses adaptive dynamic programming
    on a given MDP and policy. [Figure 21.2]"""

    class ModelMDP(MDP):
        """ Class for implementing modifed Version of input MDP with
        an editable transition model P and a custom function T. """
        def __init__(self, init, actlist, terminals, gamma, states):
            super().__init__(init, actlist, terminals, gamma)
            nested_dict = lambda: defaultdict(nested_dict)
            # StackOverflow:whats-the-best-way-to-initialize-a-dict-of-dicts-in-python
            self.P = nested_dict()

        def T(self, s, a):
            """Returns a list of tuples with probabilities for states
            based on the learnt model P."""
            return [(prob, res) for (res, prob) in self.P[(s, a)].items()]

    def __init__(self, pi, mdp):
        self.pi = pi
        self.mdp = PassiveADPAgent.ModelMDP(mdp.init, mdp.actlist,
                                            mdp.terminals, mdp.gamma, mdp.states)
        self.U = {}
        self.Nsa = defaultdict(int)
        self.Ns1_sa = defaultdict(int)
        self.s = None
        self.a = None

    def __call__(self, percept):
        s1, r1 = percept
        self.mdp.states.add(s1)  # Model keeps track of visited states.
        R, P, mdp, pi = self.mdp.reward, self.mdp.P, self.mdp, self.pi
        s, a, Nsa, Ns1_sa, U = self.s, self.a, self.Nsa, self.Ns1_sa, self.U

        if s1 not in R:  # Reward is only available for visted state.
            U[s1] = R[s1] = r1
        if s is not None:
            Nsa[(s, a)] += 1
            Ns1_sa[(s1, s, a)] += 1
            # for each t such that Ns′|sa [t, s, a] is nonzero
            for t in [res for (res, state, act), freq in Ns1_sa.items()
                      if (state, act) == (s, a) and freq != 0]:
                P[(s, a)][t] = Ns1_sa[(t, s, a)] / Nsa[(s, a)]

        U = policy_evaluation(pi, U, mdp)
        if s1 in mdp.terminals:
            self.s = self.a = None
        else:
            self.s, self.a = s1, self.pi[s1]
        return self.a

    def update_state(self, percept):
        '''To be overridden in most cases. The default case
        assumes the percept to be of type (state, reward)'''
        return percept


class PassiveTDAgent:
    """The abstract class for a Passive (non-learning) agent that uses
    temporal differences to learn utility estimates. Override update_state
    method to convert percept to state and reward. The mdp being provided
    should be an instance of a subclass of the MDP Class. [Figure 21.4]
    """

    def __init__(self, pi, mdp, alpha=None):

        self.pi = pi
        self.U = {s: 0. for s in mdp.states}
        self.Ns = {s: 0 for s in mdp.states}
        self.s = None
        self.a = None
        self.r = None
        self.gamma = mdp.gamma
        self.terminals = mdp.terminals

        if alpha:
            self.alpha = alpha
        else:
            self.alpha = lambda n: 1./(1+n)  # udacity video

    def __call__(self, percept):
        s1, r1 = self.update_state(percept)
        pi, U, Ns, s, r = self.pi, self.U, self.Ns, self.s, self.r
        alpha, gamma, terminals = self.alpha, self.gamma, self.terminals
        if not Ns[s1]:
            U[s1] = r1
        if s is not None:
            Ns[s] += 1
            U[s] += alpha(Ns[s]) * (r + gamma * U[s1] - U[s])
        if s1 in terminals:
            self.s = self.a = self.r = None
        else:
            self.s, self.a, self.r = s1, pi[s1], r1
        return self.a

    def update_state(self, percept):
        ''' To be overridden in most cases. The default case
        assumes the percept to be of type (state, reward)'''
        return percept


class QLearningAgent:
    """ An exploratory Q-learning agent. It avoids having to learn the transition
        model because the Q-value of a state can be related directly to those of
        its neighbors. [Figure 21.8]
    """
    def __init__(self, mdp, Ne, Rplus, alpha=None):

        self.gamma = mdp.gamma
        self.terminals = mdp.terminals
        self.all_act = mdp.actlist
        self.Ne = Ne  # iteration limit in exploration function
        self.Rplus = Rplus  # large value to assign before iteration limit
        self.Q = defaultdict(float)
        self.Nsa = defaultdict(float)
        self.s = None
        self.a = None
        self.r = None

        if alpha:
            self.alpha = alpha
        else:
            self.alpha = lambda n: 1./(1+n)  # udacity video

    def f(self, u, n):
        """ Exploration function. Returns fixed Rplus until
        agent has visited state, action a Ne number of times.
        Same as ADP agent in book."""
        if n < self.Ne:
            return self.Rplus
        else:
            return u

    def actions_in_state(self, state):
        """ Returns actions possible in given state.
            Useful for max and argmax. """
        if state in self.terminals:
            return [None]
        else:
            return self.all_act

    def __call__(self, percept):
        s1, r1 = self.update_state(percept)
        Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r
        alpha, gamma, terminals = self.alpha, self.gamma, self.terminals,
        actions_in_state = self.actions_in_state

        if s in terminals:
            Q[s, None] = r1
        if s is not None:
            Nsa[s, a] += 1
            Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1]
                                           for a1 in actions_in_state(s1)) - Q[s, a])
        if s in terminals:
            self.s = self.a = self.r = None
        else:
            self.s, self.r = s1, r1
            self.a = argmax(actions_in_state(s1), key=lambda a1: self.f(Q[s1, a1], Nsa[s1, a1]))
        return self.a

    def update_state(self, percept):
        ''' To be overridden in most cases. The default case
        assumes the percept to be of type (state, reward)'''
        return percept


def run_single_trial(agent_program, mdp):
    ''' Execute trial for given agent_program
    and mdp. mdp should be an instance of subclass
    of mdp.MDP '''

    def take_single_action(mdp, s, a):
        '''
        Selects outcome of taking action a
        in state s. Weighted Sampling.
        '''
        x = random.uniform(0, 1)
        cumulative_probability = 0.0
        for probability_state in mdp.T(s, a):
            probability, state = probability_state
            cumulative_probability += probability
            if x < cumulative_probability:
                break
        return state

    current_state = mdp.init
    while True:
        current_reward = mdp.R(current_state)
        percept = (current_state, current_reward)
        next_action = agent_program(percept)
        if next_action is None:
            break
        current_state = take_single_action(mdp, current_state, next_action)