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 2801–2825 of 15113 papers

TitleStatusHype
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity—0
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning—0
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement LearningCode1
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents—0
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of SkillsCode1
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
Behavior Alignment via Reward Function Optimization—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game—0
Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach—0
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households—0
Unsupervised Behavior Extraction via Random Intent Priors—0
Robust Offline Reinforcement learning with Heavy-Tailed RewardsCode0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement LearningCode1
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike—0
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data CoverageCode0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning UpdatesCode0
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Benchmark Results

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