SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 4211–4220 of 15113 papers

TitleStatusHype
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning—0
Reinforcement Learning with Elastic Time Steps—0
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning—0
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Dynamic Multi-Reward Weighting for Multi-Style Controllable GenerationCode0
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified