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 3001–3050 of 15113 papers

TitleStatusHype
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning—0
Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits—0
Convergent NMPC-based Reinforcement Learning Using Deep Expected Sarsa and Nonlinear Temporal Difference Learning—0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar—0
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization—0
Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning—0
Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation—0
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments—0
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective—0
Transforming Multimodal Models into Action Models for Radiotherapy—0
Mirror Descent Actor Critic via Bounded Advantage Learning—0
Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs—0
AI-driven materials design: a mini-review—0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate ModelCode0
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
Circular Microalgae-Based Carbon Control for Net ZeroCode0
Brief analysis of DeepSeek R1 and it's implications for Generative AI—0
Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning—0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
Reinforcement Learning for Long-Horizon Interactive LLM Agents—0
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
The Differences Between Direct Alignment Algorithms are a Blur—0
Reinforcement Learning with Segment Feedback—0
Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer—0
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs—0
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—0
Recursive generalized type-2 fuzzy radial basis function neural networks for joint position estimation and adaptive EMG-based impedance control of lower limb exoskeletonsCode0
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms—0
O-MAPL: Offline Multi-agent Preference Learning—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Model-Free RL Agents Demonstrate System 1-Like Intentionality—0
Neural Operator based Reinforcement Learning for Control of first-order PDEs with Spatially-Varying State DelayCode0
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning—0
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems—0
Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care—0
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
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Benchmark Results

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