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 3401–3450 of 15113 papers

TitleStatusHype
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Action abstractions for amortized sampling—0
Towards Effective Planning Strategies for Dynamic Opinion NetworksCode0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
Reinforcement Learning in Non-Markov Market-Making—0
Harnessing Causality in Reinforcement Learning With Bagged Decision Times—0
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
Coordinated Dispatch of Energy Storage Systems in the Active Distribution Network: A Complementary Reinforcement Learning and Optimization Approach—0
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning—0
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach—0
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge—0
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving—0
Off-dynamics Conditional Diffusion Planners—0
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous ControlCode0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide ExplorationCode0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
Neural-based Control for CubeSat Docking Maneuvers—0
SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling—0
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter—0
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards—0
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement LearningCode0
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment—0
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets—0
Multi-Objective-Optimization Multi-AUV Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Learning—0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies—0
Asymptotic Analysis of Sample-averaged Q-learning—0
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes—0
Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator—0
Generalization of Compositional Tasks with Logical Specification via Implicit Planning—0
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and ScaleCode0
Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm—0
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models—0
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree SearchCode0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments—0
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free ControlCode0
Reinforcement Learning for Control of Non-Markovian Cellular Population DynamicsCode0
Words as Beacons: Guiding RL Agents with High-Level Language Prompts—0
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL—0
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels—0
Physical Simulation for Multi-agent Multi-machine Tending—0
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment—0
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Benchmark Results

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