text: ''' QLearningAgentClass.py: Class for a basic QLearningAgent '''
  
  # Python imports.
  import random
  import numpy
  import time
  from collections import defaultdict
  
  # Other imports.
  from simple_rl.agents.AgentClass import Agent
  
  class QLearningAgent(Agent):
      ''' Implementation for a Q Learning Agent '''
  
      def __init__(self, actions, name="Q-learning", alpha=0.1, gamma=0.99, epsilon=0.1, explore="uniform", anneal=False, custom_q_init=None, default_q=0):
          '''
          Args:
              actions (list): Contains strings denoting the actions.
              name (str): Denotes the name of the agent.
              alpha (float): Learning rate.
              gamma (float): Discount factor.
              epsilon (float): Exploration term.
              explore (str): One of {softmax, uniform}. Denotes explore policy.
              custom_q_init (defaultdict{state, defaultdict{action, float}}): a dictionary of dictionaries storing the initial q-values. Can be used for potential shaping (Wiewiora, 2003)
              default_q (float): the default value to initialize every entry in the q-table with [by default, set to 0.0]
          '''
          name_ext = "-" + explore if explore != "uniform" else ""
          Agent.__init__(self, name=name + name_ext, actions=actions, gamma=gamma)
  
          # Set/initialize parameters and other relevant classwide data
          self.alpha, self.alpha_init = alpha, alpha
          self.epsilon, self.epsilon_init = epsilon, epsilon
          self.step_number = 0
          self.anneal = anneal
          self.default_q = default_q # 0 # 1 / (1 - self.gamma)
          self.explore = explore
          self.custom_q_init = custom_q_init
  
          # Q Function:
          if self.custom_q_init:
              self.q_func = self.custom_q_init
          else:
              self.q_func = defaultdict(lambda: defaultdict(lambda: self.default_q))
          
          # Key: state
          # Val: dict
              #   Key: action
              #   Val: q-value
  
  
      def get_parameters(self):
          '''
          Returns:
              (dict) key=param_name (str) --> val=param_val (object).
          '''
          param_dict = defaultdict(int)
  
          param_dict["alpha"] = self.alpha
          param_dict["gamma"] = self.gamma
          param_dict["epsilon"] = self.epsilon_init
          param_dict["anneal"] = self.anneal
          param_dict["explore"] = self.explore
  
          return param_dict
  
      # --------------------------------
      # ---- CENTRAL ACTION METHODS ----
      # --------------------------------
  
      def act(self, state, reward, learning=True):
          '''
          Args:
              state (State)
              reward (float)
  
          Returns:
              (str)
  
          Summary:
              The central method called during each time step.
              Retrieves the action according to the current policy
              and performs updates given (s=self.prev_state,
              a=self.prev_action, r=reward, s'=state)
          '''
          if learning:
              self.update(self.prev_state, self.prev_action, reward, state)
          if self.explore == "softmax":
              # Softmax exploration
              action = self.soft_max_policy(state)
          else:
              # Uniform exploration
              action = self.epsilon_greedy_q_policy(state)
  
          self.prev_state = state
          self.prev_action = action
          self.step_number += 1
  
          # Anneal params.
          if learning and self.anneal:
              self._anneal()
  
          return action
  
      def epsilon_greedy_q_policy(self, state):
          '''
          Args:
              state (State)
  
          Returns:
              (str): action.
          '''
          # Policy: Epsilon of the time explore, otherwise, greedyQ.
          if numpy.random.random() > self.epsilon:
              # Exploit.
              action = self.get_max_q_action(state)
          else:
              # Explore
              action = numpy.random.choice(self.actions)
  
          return action
  
      def soft_max_policy(self, state):
          '''
          Args:
              state (State): Contains relevant state information.
  
          Returns:
              (str): action.
          '''
          return numpy.random.choice(self.actions, 1, p=self.get_action_distr(state))[0]
  
      # ---------------------------------
      # ---- Q VALUES AND PARAMETERS ----
      # ---------------------------------
  
      def update(self, state, action, reward, next_state):
          '''
          Args:
              state (State)
              action (str)
              reward (float)
              next_state (State)
  
          Summary:
              Updates the internal Q Function according to the Bellman Equation. (Classic Q Learning update)
          '''
          # If this is the first state, just return.
          if state is None:
              self.prev_state = next_state
              return
  
          # Update the Q Function.
          max_q_curr_state = self.get_max_q_value(next_state)
          prev_q_val = self.get_q_value(state, action)
          self.q_func[state][action] = (1 - self.alpha) * prev_q_val + self.alpha * (reward + self.gamma*max_q_curr_state)
          
  
      def _anneal(self):
          # Taken from "Note on learning rate schedules for stochastic optimization, by Darken and Moody (Yale)":
          self.alpha = self.alpha_init / (1.0 +  (self.step_number / 1000.0)*(self.episode_number + 1) / 2000.0 )
          self.epsilon = self.epsilon_init / (1.0 + (self.step_number / 1000.0)*(self.episode_number + 1) / 2000.0 )
  
      def _compute_max_qval_action_pair(self, state):
          '''
          Args:
              state (State)
  
          Returns:
              (tuple) --> (float, str): where the float is the Qval, str is the action.
          '''
          # Grab random initial action in case all equal
          best_action = random.choice(self.actions)
          max_q_val = float("-inf")
          shuffled_action_list = self.actions[:]
          random.shuffle(shuffled_action_list)
  
          # Find best action (action w/ current max predicted Q value)
          for action in shuffled_action_list:
              q_s_a = self.get_q_value(state, action)
              if q_s_a > max_q_val:
                  max_q_val = q_s_a
                  best_action = action
  
          return max_q_val, best_action
  
      def get_max_q_action(self, state):
          '''
          Args:
              state (State)
  
          Returns:
              (str): denoting the action with the max q value in the given @state.
          '''
          return self._compute_max_qval_action_pair(state)[1]
  
      def get_max_q_value(self, state):
          '''
          Args:
              state (State)
  
          Returns:
              (float): denoting the max q value in the given @state.
          '''
          return self._compute_max_qval_action_pair(state)[0]
  
      def get_value(self, state):
          '''
          Args:
              state (State)
  
          Returns:
              (float)
          '''
          return self.get_max_q_value(state)
  
      def get_q_value(self, state, action):
          '''
          Args:
              state (State)
              action (str)
  
          Returns:
              (float): denoting the q value of the (@state, @action) pair.
          '''
          return self.q_func[state][action]
  
      def get_action_distr(self, state, beta=0.2):
          '''
          Args:
              state (State)
              beta (float): Softmax temperature parameter.
  
          Returns:
              (list of floats): The i-th float corresponds to the probability
              mass associated with the i-th action (indexing into self.actions)
          '''
          all_q_vals = []
          for i, action in enumerate(self.actions):
              all_q_vals.append(self.get_q_value(state, action))
  
          # Softmax distribution.
          total = sum([numpy.exp(beta * qv) for qv in all_q_vals])
          softmax = [numpy.exp(beta * qv) / total for qv in all_q_vals]
  
          return softmax
  
      def reset(self):
          self.step_number = 0
          self.episode_number = 0
          if self.custom_q_init:
              self.q_func = self.custom_q_init
          else:
              self.q_func = defaultdict(lambda : defaultdict(lambda: self.default_q))
          Agent.reset(self)
  
      def end_of_episode(self):
          '''
          Summary:
              Resets the agents prior pointers.
          '''
          if self.anneal:
              self._anneal()
          Agent.end_of_episode(self)
  
      def print_v_func(self):
          '''
          Summary:
              Prints the V function.
          '''
          for state in self.q_func.keys():
              print(state, self.get_value(state))
  
      def print_q_func(self):
          '''
          Summary:
              Prints the Q function.
          '''
          if len(self.q_func) == 0:
              print("Q Func empty!")
          else:
              for state, actiond in self.q_func.items():
                  print(state)
                  for action, q_val in actiond.items():
                      print("    ", action, q_val)
  