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 1201–1250 of 15113 papers

TitleStatusHype
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories—0
RLZero: Direct Policy Inference from Language Without In-Domain Supervision—0
Reinforcement Learning Enhanced LLMs: A SurveyCode3
Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement LearningCode1
Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline PolicyCode0
Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting—0
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy—0
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning—0
Using Deep Reinforcement Learning to Enhance Channel Sampling Patterns in Integrated Sensing and Communication—0
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning—0
AI-Driven Day-to-Day Route ChoiceCode1
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator—0
Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement LearningCode0
Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents—0
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum—0
AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms—0
Generating Critical Scenarios for Testing Automated Driving Systems—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Conformal Symplectic Optimization for Stable Reinforcement LearningCode2
Reinforcement learning to learn quantum states for Heisenberg scaling accuracyCode0
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication—0
Approximately Optimal Search on a Higher-dimensional Sliding PuzzleCode0
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks—0
Explore Reinforced: Equilibrium Approximation with Reinforcement Learning—0
Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic PerspectiveCode2
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Provable Partially Observable Reinforcement Learning with Privileged Information—0
Bilinear Convolution Decomposition for Causal RL Interpretability—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
o1-Coder: an o1 Replication for CodingCode3
RL-MILP Solver: A Reinforcement Learning Approach for Solving Mixed-Integer Linear Programs with Graph Neural Networks—0
HVAC-DPT: A Decision Pretrained Transformer for HVAC Control—0
Solving Rubik's Cube Without Tricky Sampling—0
Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in FeedCode0
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning—0
Convex Regularization and Convergence of Policy Gradient Flows under Safety Constraints—0
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges—0
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning—0
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics—0
ScaleViz: Scaling Visualization Recommendation Models on Large Data—0
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware—0
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement—0
Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative TradingCode2
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards—0
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble—0
Free^2Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models—0
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling—0
Probing for Consciousness in Machines—0
Unsupervised Event Outlier Detection in Continuous Time—0
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Benchmark Results

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