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 1976–2000 of 15113 papers

TitleStatusHype
Causal prompting model-based offline reinforcement learning—0
REvolve: Reward Evolution with Large Language Models using Human Feedback—0
A Fast Convergence Theory for Offline Decision Making—0
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RLCode0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
MOSEAC: Streamlined Variable Time Step Reinforcement LearningCode0
NeoRL: Efficient Exploration for Nonepisodic RL—0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems—0
Learning the Target Network in Function Space—0
Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming—0
A Digital Twin Framework for Reinforcement Learning with Real-Time Self-Improvement via Human Assistive Teleoperation—0
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement LearningCode1
Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)Code0
SUBER: An RL Environment with Simulated Human Behavior for Recommender SystemsCode1
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
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