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 2601–2650 of 15113 papers

TitleStatusHype
Generalization in Monitored Markov Decision Processes (Mon-MDPs)—0
Preference Optimization for Combinatorial Optimization Problems—0
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
Adaptive Diffusion Policy Optimization for Robotic ManipulationCode0
DSADF: Thinking Fast and Slow for Decision Making—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains—0
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
Learning Value of Information towards Joint Communication and Control in 6G V2X—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback—0
LineFlow: A Framework to Learn Active Control of Production LinesCode0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning—0
Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs—0
Multi-agent Embodied AI: Advances and Future Directions—0
On Corruption-Robustness in Performative Reinforcement Learning—0
USPR: Learning a Unified Solver for Profiled RoutingCode0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles—0
Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search—0
Large Language Models are Autonomous Cyber DefendersCode0
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers—0
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization—0
Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
The Steganographic Potentials of Language Models—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-TuningCode0
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study—0
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning—0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
A Generalised and Adaptable Reinforcement Learning Stopping MethodCode0
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Benchmark Results

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