import numpy as np
from copy import deepcopy
from mushroom_rl.algorithms.value.td import TD
from mushroom_rl.approximators.ensemble_table import EnsembleTable
[docs]class MaxminQLearning(TD):
"""
Maxmin Q-Learning algorithm without replay memory.
"Maxmin Q-learning: Controlling the Estimation Bias of Q-learning".
Lan Q. et al. 2019.
"""
[docs] def __init__(self, mdp_info, policy, learning_rate, n_tables):
"""
Constructor.
Args:
n_tables (int): number of tables in the ensemble.
"""
self._n_tables = n_tables
Q = EnsembleTable(n_tables, mdp_info.size, prediction='min')
super().__init__(mdp_info, policy, Q, learning_rate)
self._alpha_mm = [deepcopy(self._alpha) for _ in range(n_tables)]
self._add_save_attr(_n_tables='primitive', _alpha_mm='primitive')
[docs] def _update(self, state, action, reward, next_state, absorbing):
approximator_idx = np.random.choice(self._n_tables)
q_current = self.Q[approximator_idx][state, action]
if not absorbing:
q_ss = self.Q.predict(next_state)
q_next = np.max(q_ss)
else:
q_next = 0.
q = q_current + self._alpha_mm[approximator_idx](state, action) * (
reward + self.mdp_info.gamma * q_next - q_current)
self.Q[approximator_idx][state, action] = q