Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid locomotion. Besides, the optimality of desired Boltzmann policy set for non-optimal soft value function is not persuasive enough. In this paper, we first derive soft policy gradient based on entropy regularized expected reward objective for RL with continuous actions. Then, we present an off-policy actor-critic, model-free maximum entropy deep RL algorithm called deep soft policy gradient (DSPG) by combining soft policy gradient with soft Bellman equation. To ensure stable learning while eliminating the need of two separate critics for soft value functions, we leverage double sampling approach to making the soft Bellman equation tractable. The experimental results demonstrate that our method outperforms in performance over off-policy prior methods.

本文提出了一种新的深度强化学习算法，利用基于熵正则化的期望回报目标推导出软策略梯度，将其与软Bellman方程相结合，得到了名为DSPG的最大熵深度强化学习算法，该算法采用双重采样方法确保学习的稳定性，有效提高了表现，克服了已有方法在大规模离线数据训练以及具有高维动作状态问题的稳定性不足等问题。

最大熵深度强化学习的软策略梯度方法