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 4291–4300 of 15113 papers

TitleStatusHype
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
Rethinking the Role of Proxy Rewards in Language Model AlignmentCode0
To the Max: Reinventing Reward in Reinforcement LearningCode0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
The Political Preferences of LLMs—0
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous EnvironmentsCode0
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation LearningCode0
Causal Coordinated Concurrent Reinforcement Learning—0
Show:102550
← PrevPage 430 of 1512Next →

Benchmark Results

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