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 4451–4500 of 15113 papers

TitleStatusHype
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman OperatorCode0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
Safety-Enhanced Self-Learning for Optimal Power Converter Control—0
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer—0
Language Model Alignment with Elastic ResetCode0
Evaluation of Active Feature Acquisition Methods for Static Feature Settings—0
Diffused Task-Agnostic Milestone Planner—0
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks—0
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications—0
LExCI: A Framework for Reinforcement Learning with Embedded SystemsCode0
Contact Energy Based Hindsight Experience Prioritization—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
MASP: Scalable GNN-based Planning for Multi-Agent Navigation—0
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption—0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators—0
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning—0
Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices—0
Adaptive operator selection utilising generalised experience—0
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities—0
Learning Curricula in Open-Ended Worlds—0
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning—0
Self-Critical Alternate Learning based Semantic Broadcast Communication—0
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning—0
DDxT: Deep Generative Transformer Models for Differential DiagnosisCode0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk—0
Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection (extended version)Code0
Safe Reinforcement Learning in Tensor Reproducing Kernel Hilbert Space—0
Optimal Attack and Defense for Reinforcement LearningCode0
Predictable Reinforcement Learning Dynamics through Entropy Rate MinimizationCode0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement LearningCode0
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning—0
Two-Step Reinforcement Learning for Multistage Strategy Card Game—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Two-step dynamic obstacle avoidanceCode0
An Investigation of Time Reversal Symmetry in Reinforcement LearningCode0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Replay across Experiments: A Natural Extension of Off-Policy RL—0
Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement LearningCode0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and ConstraintsCode0
Digital Twin-Native AI-Driven Service Architecture for Industrial Networks—0
Evaluating Pretrained models for Deployable Lifelong Learning—0
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Benchmark Results

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