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 1991–2000 of 15113 papers

TitleStatusHype
Bayesian Design Principles for Offline-to-Online Reinforcement LearningCode0
Enhancing Efficiency of Safe Reinforcement Learning via Sample ManipulationCode5
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement LearningCode1
Reinforcement Learning for Sociohydrology—0
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-ThoughtCode1
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents—0
Hybrid Reinforcement Learning Framework for Mixed-Variable Problems—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity—0
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Benchmark Results

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