class_docstr: Q-learning agent with tabular representation.
class_docstr_tok: ["Q-learning","agent","with","tabular","representation","."]
docstr: Epsilon greedy policy.
docstr_tok: ["Epsilon","greedy","policy","."]
code: def _choose_action(self, state):
      """Epsilon greedy policy."""
      if np.random.uniform(0, 1) < self._epsilon:
          return np.random.randint(self._num_actions), "RANDOM"
      else:
          return np.argmax(self._q[state]), "GREEDY"
  
file_path: CNTK/bindings/python/cntk/contrib/deeprl/agent/tabular_qlearning.py
imports: import copy
  import numpy
  from .agent import AgentBaseClass
  from .shared.qlearning_parameters import QLearningParameters
label_desc: Use image node_id 11 for calling the TabularQLearning obj's underlying member method code with example usage: obj._choose_action(state) and returns: np,  str, np,  str