Agent.py

import numpy as np
from collections import defaultdict

class Agent:
    def update_Q(self, Qsa, Qsa_next, reward):
       return Qsa + (self.alpha * (reward + (self.gamma * Qsa_next) - Qsa))
	
    def epsilon_greedy_probs(self, Q_s, i_episode, eps=None):
       epsilon = 1.0 / i_episode
       if eps is not None:
           epsilon = eps
       policy_s = np.ones(self.nA) * epsilon / self.nA
       policy_s[np.argmax(Q_s)] = 1 - epsilon + (epsilon / self.nA)
       return policy_s
    
    def __init__(self, nA=6):
        """ Initialize agent.

        Params
        ======
        - nA: number of actions available to the agent
        """
        self.nA = nA
        self.Q = defaultdict(lambda: np.zeros(self.nA))
        self.epsilon = 0.005
        self.alpha = 0.3
        self.gamma = 1.0
        self.policy_s = None
        self.i_episode = 1

    def select_action(self,state):
        """ Given the state, select an action.

        Params
        ======
        - state: the current state of the environment

        Returns
        =======
        - action: an integer, compatible with the task's action space
        """
        self.policy_s = self.epsilon_greedy_probs(self.Q[state],self.i_episode)
        self.i_episode += 1
        return np.random.choice(np.arange(self.nA),p = self.policy_s)

    def step(self, state, action, reward, next_state, done):
        """ Update the agent's knowledge, using the most recently sampled tuple.

        Params
        ======
        - state: the previous state of the environment
        - action: the agent's previous choice of action
        - reward: last reward received
        - next_state: the current state of the environment
        - done: whether the episode is complete (True or False)
        """
        self.Q[state][action] = self.update_Q(self.Q[state][action],np.max(self.Q[next_state]),\
                                           reward)