Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with $k$-step Policy Gradients
Researchers propose k-step policy gradients to address a fundamental limitation in reinforcement learning: standard policy gradient methods optimize greedily based only on immediate one-step returns, causing them to converge to suboptimal solutions when policy classes are restricted. The new approach couples randomness across multiple timesteps to escape these local optima, with theoretical guarantees that performance approaches the optimal deterministic policy exponentially as k increases. This work matters for practitioners deploying RL in constrained settings, from robotics to dialogue systems, where restricted policy classes are common but myopic optimization has historically limited performance ceilings.
Modelwire context
ExplainerThe paper's actual contribution is narrower than it sounds: k-step policy gradients work only for restricted policy classes (think: linear policies, tabular settings), not the neural network policies that dominate modern RL. The exponential convergence guarantee applies to approaching the best deterministic policy within that restricted class, not the global optimum.
This connects to the equivariant RL work on quantum circuit synthesis from the same day. Both papers tackle a shared bottleneck: how to optimize when your policy class is structurally constrained (here, by design; there, by symmetry). The quantum paper embeds constraints into the architecture itself. This paper instead proposes a training procedure that works around constraints. Neither solves the deeper question of whether restricted policies are the right choice for a problem, but together they suggest the field is converging on the idea that constraints are features, not bugs, when deployed correctly.
If follow-up work demonstrates k-step policy gradients matching or beating standard methods on continuous control benchmarks (MuJoCo, robotics) using neural network policies, the restriction to tabular/linear settings was an artifact of the proof technique, not the method. If it doesn't, the practical scope remains niche: dialogue systems, constrained robotics, and other domains where policy classes are genuinely limited by hardware or safety requirements.
Coverage we drew on
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MentionsPolicy Gradients · Reinforcement Learning · Mirror Descent · Projected Gradient Descent
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