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 4261–4270 of 15113 papers

TitleStatusHype
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning—0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Convergence for Natural Policy Gradient on Infinite-State Queueing MDPs—0
Code as Reward: Empowering Reinforcement Learning with VLMs—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs—0
Averaging n-step Returns Reduces Variance in Reinforcement Learning—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
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Benchmark Results

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