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 3876–3900 of 15113 papers

TitleStatusHype
MOSEAC: Streamlined Variable Time Step Reinforcement LearningCode0
A Fast Convergence Theory for Offline Decision Making—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
NeoRL: Efficient Exploration for Nonepisodic RL—0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
REvolve: Reward Evolution with Large Language Models using Human Feedback—0
Causal prompting model-based offline reinforcement learning—0
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RLCode0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems—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
Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)Code0
Bayesian Design Principles for Offline-to-Online Reinforcement LearningCode0
Reinforcement Learning for Sociohydrology—0
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
Hybrid Reinforcement Learning Framework for Mixed-Variable Problems—0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
Efficient Stimuli Generation using Reinforcement Learning in Design Verification—0
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning—0
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Benchmark Results

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