RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.
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UNVERDICTED 2representative citing papers
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.
citing papers explorer
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RL4RLA: Teaching ML to Discover Randomized Linear Algebra Algorithms Through Curriculum Design and Graph-Based Search
RL4RLA is a reinforcement learning framework that discovers interpretable symbolic randomized linear algebra algorithms by combining curriculum learning and graph-based search to overcome sparse rewards and large search spaces.
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Using Common Random Numbers for Simulation-based Planning with Rollouts
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.