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 7501–7525 of 15113 papers

TitleStatusHype
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning—0
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures—0
Incentivizing an Unknown Crowd—0
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph ForecastingCode1
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution MatchingCode0
User Tampering in Reinforcement Learning Recommender Systems—0
Self-supervised Reinforcement Learning with Independently Controllable Subgoals—0
PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution SystemsCode1
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
A Deep Reinforcement Learning Approach for Online Parcel Assignment—0
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent—0
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions—0
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning—0
Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning—0
CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems—0
Robust Predictable Control—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement LearningCode2
Optimizing Quantum Variational Circuits with Deep Reinforcement LearningCode1
On the impact of MDP design for Reinforcement Learning agents in Resource Management—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Delving into Macro Placement with Reinforcement Learning—0
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented GuesserCode0
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning—0
Guiding Global Placement With Reinforcement Learning—0
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Benchmark Results

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