  Recently deep reinforcement learning (DRL) has achieved outstanding success
on solving many difficult and large-scale RL problems. However the high sample
cost required for effective learning often makes DRL unaffordable in
resource-limited applications. With the aim of improving sample efficiency and
learning performance, we will develop a new DRL algorithm in this paper that
seamless integrates entropy-induced and bootstrap-induced techniques for
efficient and deep exploration of the learning environment. Specifically, a
general form of Tsallis entropy regularizer will be utilized to drive
entropy-induced exploration based on efficient approximation of optimal
action-selection policies. Different from many existing works that rely on
action dithering strategies for exploration, our algorithm is efficient in
exploring actions with clear exploration value. Meanwhile, by employing an
ensemble of Q-networks under varied Tsallis entropy regularization, the
diversity of the ensemble can be further enhanced to enable effective
bootstrap-induced exploration. Experiments on Atari game playing tasks clearly
demonstrate that our new algorithm can achieve more efficient and effective
exploration for DRL, in comparison to recently proposed exploration methods
including Bootstrapped Deep Q-Network and UCB Q-Ensemble.
