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 1771–1780 of 15113 papers

TitleStatusHype
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—0
Reinforcement Learning: Tutorial and Survey—0
Learning Goal-Conditioned Representations for Language Reward ModelsCode1
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction—0
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving GeneralizationCode0
Random Latent Exploration for Deep Reinforcement Learning—0
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and ReviewCode2
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning—0
Chip Placement with Diffusion ModelsCode1
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Benchmark Results

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