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 2676–2700 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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