Soft $Q(λ)$: A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces
Soft Q-learning has emerged as a versatile model-free method for entropy-regularised reinforcement learning, optimising for returns augmented with a penalty on the divergence from a reference policy. Despite its success, the multi-step extensions of soft Q-learning remain relatively unexplored and limited to on-policy action sampling under the Boltzmann policy. In this brief research note, we first present a formal $n$-step formulation for soft Q-learning and then extend this framework to the fully off-policy case by introducing a novel Soft Tree Backup operator. Finally, we unify these developments into Soft $Q(λ)$, an elegant online, off-policy, eligibility trace framework that allows f...
| Authors | Pranav Mahajan, Ben Seymour |
|---|---|
| Primary category | cs.LG |
| Published | 15 April 2026 |
| Last updated at source | 17 July 2026 |
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