An Alternative Softmax Operator for Reinforcement Learning
This work addresses a specific issue in reinforcement learning for decision-making systems, offering an incremental improvement over existing methods.
The paper tackled the problem of the Boltzmann softmax operator's misbehavior in reinforcement learning by introducing a differentiable, non-expansion softmax operator that ensures convergence. The result was a variant of the SARSA algorithm that computes a Boltzmann policy with state-dependent temperature, showing favorable performance in practice.
A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study a differentiable softmax operator that, among other properties, is a non-expansion ensuring a convergent behavior in learning and planning. We introduce a variant of SARSA algorithm that, by utilizing the new operator, computes a Boltzmann policy with a state-dependent temperature parameter. We show that the algorithm is convergent and that it performs favorably in practice.