Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions
This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inclusions. On the RL side, the estimation bias inherent in conventional regularization schemes is addressed by augmenting classical least-squares temporal-difference (LSTD) policy evaluation with the sparsity-inducing, non-convex projected minimax concave (PMC) penalty. Because the PMC penalty is weakly convex, the resulting fixed-point problem is no longer monotone; instead, it falls under a broader class of non-monotone inclusions involving the sum of a monotone Lipschitz operator and a hypomonotone operator. On the theory side, novel conv...
| Authors | Kyohei Suzuki, Konstantinos Slavakis |
|---|---|
| Primary category | cs.LG |
| Published | 6 July 2026 |
| Last updated at source | 7 July 2026 |
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