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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 391–400 of 15113 papers

TitleStatusHype
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization—0
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs—0
Optimizing Anytime Reasoning via Budget Relative Policy OptimizationCode2
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning—0
Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach—0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning—0
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement LearningCode3
Synthetic Data RL: Task Definition Is All You NeedCode2
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents—0
Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning—0
Show:102550
← PrevPage 40 of 1512Next →

Benchmark Results

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