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 911–920 of 15113 papers

TitleStatusHype
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—0
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—0
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?Code0
Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information—0
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
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation—0
Dynamic Reinforcement Learning for Actors—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
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Benchmark Results

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