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 2651–2700 of 15113 papers

TitleStatusHype
The Effective Horizon Explains Deep RL Performance in Stochastic EnvironmentsCode1
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
An Invitation to Deep Reinforcement Learning—0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency—0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL ApproachCode1
Sequential Planning in Large Partially Observable Environments guided by LLMsCode1
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Reward Certification for Policy Smoothed Reinforcement LearningCode0
Spreeze: High-Throughput Parallel Reinforcement Learning Framework—0
KnowGPT: Knowledge Graph based Prompting for Large Language Models—0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks—0
The Generalization Gap in Offline Reinforcement LearningCode1
Evolving Reservoirs for Meta Reinforcement LearningCode2
On the calibration of compartmental epidemiological modelsCode0
PerfRL: A Small Language Model Framework for Efficient Code Optimization—0
Guaranteed Trust Region Optimization via Two-Phase KL Penalization—0
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal ControlCode1
Modeling Risk in Reinforcement Learning: A Literature Mapping—0
Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization—0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
Learning to sample in Cartesian MRI—0
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman OperatorCode0
Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement LearningCode0
CODEX: A Cluster-Based Method for Explainable Reinforcement LearningCode0
Safety-Enhanced Self-Learning for Optimal Power Converter Control—0
Language Model Alignment with Elastic ResetCode0
Pearl: A Production-ready Reinforcement Learning AgentCode4
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks—0
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer—0
Diffused Task-Agnostic Milestone Planner—0
Evaluation of Active Feature Acquisition Methods for Static Feature Settings—0
Mitigating Open-Vocabulary Caption HallucinationsCode1
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption—0
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications—0
Contact Energy Based Hindsight Experience Prioritization—0
MASP: Scalable GNN-based Planning for Multi-Agent Navigation—0
LExCI: A Framework for Reinforcement Learning with Embedded SystemsCode0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Adaptive operator selection utilising generalised experience—0
Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices—0
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators—0
Show:102550
← PrevPage 54 of 303Next →

Benchmark Results

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