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 2971–2980 of 15113 papers

TitleStatusHype
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?Code0
Tackling the Zero-Shot Reinforcement Learning Loss Directly—0
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents—0
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Dynamic Reinforcement Learning for Actors—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
A Survey of Reinforcement Learning for Optimization in Automation—0
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Benchmark Results

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