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 1961–1970 of 15113 papers

TitleStatusHype
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms—0
Prompt-based Visual Alignment for Zero-shot Policy Transfer—0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning—0
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning—0
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement LearningCode1
Reinforcement Learning with Lookahead Information—0
Rectifying Reinforcement Learning for Reward Matching—0
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
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Benchmark Results

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