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 5401–5425 of 15113 papers

TitleStatusHype
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem—0
Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning—0
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
InfoRL: Interpretable Reinforcement Learning using Information Maximization—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems—0
Information Maximizing Exploration with a Latent Dynamics Model—0
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL—0
Information Theoretically Aided Reinforcement Learning for Embodied Agents—0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
Information-Theoretic Considerations in Batch Reinforcement Learning—0
Information Theoretic Model Predictive Q-Learning—0
Information-theoretic Task Selection for Meta-Reinforcement Learning—0
Informing Autonomous Deception Systems with Cyber Expert Performance Data—0
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
Inherently Explainable Reinforcement Learning in Natural Language—0
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks—0
Innate-Values-driven Reinforcement Learning based Cooperative Multi-Agent Cognitive Modeling—0
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents—0
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning—0
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback—0
Instabilities of Offline RL with Pre-Trained Neural Representation—0
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Benchmark Results

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