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 13601–13650 of 15113 papers

TitleStatusHype
Interpretable Rationale Augmented Charge Prediction System—0
Distantly Supervised NER with Partial Annotation Learning and Reinforcement LearningCode0
Learning Dexterous In-Hand Manipulation—0
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System—0
Count-Based Exploration with the Successor RepresentationCode0
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning—0
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning—0
A Reinforcement Learning Approach to Target Tracking in a Camera Network—0
Backprop-Q: Generalized Backpropagation for Stochastic Computation GraphsCode0
Multi-Agent Reinforcement Learning: A Report on Challenges and ApproachesCode0
Variational Bayesian Reinforcement Learning with Regret Bounds—0
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior—0
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences—0
Learning to Play Pong using Policy Gradient Learning—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors—0
NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images—0
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement LearningCode0
FuzzerGym: A Competitive Framework for Fuzzing and Learning—0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts—0
Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning—0
News-based trading strategies—0
Representational efficiency outweighs action efficiency in human program induction—0
Backplay: "Man muss immer umkehren"Code0
Deep Reinforcement Learning for Swarm SystemsCode0
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning GameCode0
Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specifications—0
Safe Reinforcement Learning via Probabilistic Shields—0
Remember and Forget for Experience ReplayCode0
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees—0
Online Robust Policy Learning in the Presence of Unknown Adversaries—0
Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms—0
Bipedal Walking Robot using Deep Deterministic Policy GradientCode0
Exploring Hierarchy-Aware Inverse Reinforcement Learning—0
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario—0
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach—0
Will it Blend? Composing Value Functions in Reinforcement Learning—0
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical GuaranteesCode0
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving—0
Video Summarisation by Classification with Deep Reinforcement Learning—0
Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images—0
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure—0
Financial Trading as a Game: A Deep Reinforcement Learning ApproachCode0
End-to-End Race Driving with Deep Reinforcement Learning—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks—0
Arcades: A deep model for adaptive decision making in voice controlled smart-home—0
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Benchmark Results

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