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 2101–2150 of 15113 papers

TitleStatusHype
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian OptimizationCode0
Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models—0
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback—0
Neural Network Compression for Reinforcement Learning Tasks—0
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning—0
Fairness in Reinforcement Learning: A Survey—0
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies—0
Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning—0
Dominion: A New Frontier for AI Research—0
Value Augmented Sampling for Language Model Alignment and PersonalizationCode1
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning—0
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach—0
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies—0
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language ModelCode9
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language ModelsCode1
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows—0
Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement LearningCode2
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints—0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning—0
Proximal Curriculum with Task Correlations for Deep Reinforcement LearningCode0
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
Model-based reinforcement learning for protein backbone design—0
Learning Optimal Deterministic Policies with Stochastic Policy Gradients—0
Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk—0
Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient ManipulationCode5
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation—0
Reinforcement Learning-Guided Semi-Supervised Learning—0
Constrained Reinforcement Learning Under Model Mismatch—0
Tabular and Deep Reinforcement Learning for Gittins Index—0
FLAME: Factuality-Aware Alignment for Large Language Models—0
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks—0
Learning Force Control for Legged Manipulation—0
Queue-based Eco-Driving at Roundabouts with Reinforcement Learning—0
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPOCode1
Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning—0
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning—0
Towards Generalist Robot Learning from Internet Video: A Survey—0
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement LearningCode0
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Benchmark Results

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