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 8451–8475 of 15113 papers

TitleStatusHype
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning—0
Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems—0
Projected State-action Balancing Weights for Offline Reinforcement Learning—0
User Tampering in Reinforcement Learning Recommender Systems—0
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution MatchingCode0
Self-supervised Reinforcement Learning with Independently Controllable Subgoals—0
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures—0
Incentivizing an Unknown Crowd—0
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions—0
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent—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
A Deep Reinforcement Learning Approach for Online Parcel Assignment—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Robust Predictable Control—0
On the impact of MDP design for Reinforcement Learning agents in Resource Management—0
CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems—0
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning—0
Delving into Macro Placement with Reinforcement Learning—0
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented GuesserCode0
Guiding Global Placement With Reinforcement Learning—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning—0
Temporal Shift Reinforcement LearningCode0
Show:102550
← PrevPage 339 of 605Next →

Benchmark Results

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