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 3026–3050 of 15113 papers

TitleStatusHype
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