TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
A closer look at invalid action masking in policy gradient algorithms
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7verdicts
UNVERDICTED 7representative citing papers
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
AlphaTransit pairs MCTS with a learned policy-value network to reach 54.6% and 82.1% service rates on a Bloomington transit benchmark, outperforming plain RL and plain MCTS baselines.
Bellman-Taylor score decoding framework for MDPs with implicit state-dependent action constraints, enabling standard DRL optimization with a decomposed optimality gap guarantee.
DRL with PPO learns selective pairwise XOR merging policies for deadline-constrained coded caching, reducing broadcast-packet expiration ratio by 40.9% versus SACM++ on uniform-demand benchmarks.
TARMM uses a temporal graph to model RAN dynamics and MARL with action masking for proactive mobility management in 5G O-RAN, reducing tail latency by up to 44% and packet loss by up to 56% on a multi-cell testbed for VR workloads.
A deep RL scheduler with macro-action blocks and constraint masking was built and deployed for NASA's Carruthers Geocorona Observatory, generating feasible schedules faster than heuristics.
citing papers explorer
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AlphaTransit: Learning to Design City-scale Transit Routes
AlphaTransit pairs MCTS with a learned policy-value network to reach 54.6% and 82.1% service rates on a Bloomington transit benchmark, outperforming plain RL and plain MCTS baselines.
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Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
Bellman-Taylor score decoding framework for MDPs with implicit state-dependent action constraints, enabling standard DRL optimization with a decomposed optimality gap guarantee.