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 2021–2030 of 15113 papers

TitleStatusHype
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM AlignmentCode0
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model ScalesCode0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Q-value Regularized Transformer for Offline Reinforcement LearningCode1
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld—0
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation—0
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement LearningCode0
Rethinking Transformers in Solving POMDPsCode1
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing ProblemsCode1
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Benchmark Results

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