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 3551–3575 of 15113 papers

TitleStatusHype
Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers—0
Robust Reinforcement Learning with Dynamic Distortion Risk MeasuresCode0
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies—0
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema—0
KAN v.s. MLP for Offline Reinforcement Learning—0
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Quasimetric Value Functions with Dense Rewards—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications—0
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning—0
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search StrategiesCode0
Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning—0
Hand-Object Interaction Pretraining from Videos—0
Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence—0
The Role of Deep Learning Regularizations on Actors in Offline RLCode0
Learning Efficient Recursive Numeral Systems via Reinforcement Learning—0
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout—0
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
Semifactual Explanations for Reinforcement LearningCode0
Forward KL Regularized Preference Optimization for Aligning Diffusion Policies—0
Markov Chain Variance Estimation: A Stochastic Approximation Approach—0
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Benchmark Results

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