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 3911–3920 of 15113 papers

TitleStatusHype
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective—0
Large Language Model-Driven Curriculum Design for Mobile NetworksCode0
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted RegressionCode0
Oracle-Efficient Reinforcement Learning for Max Value Ensembles—0
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement LearningCode0
Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments—0
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model ScalesCode0
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld—0
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Benchmark Results

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