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 9201–9225 of 15113 papers

TitleStatusHype
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
Approximate Robust NMPC using Reinforcement Learning—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Machine Learning Applications in the Routing in Computer Networks—0
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems—0
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems—0
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation—0
Influencing Reinforcement Learning through Natural Language GuidanceCode0
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA—0
A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks—0
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?Code0
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning—0
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection—0
Trajectory Tracking of Underactuated Sea Vessels With Uncertain Dynamics: An Integral Reinforcement Learning Approach—0
Optimization Algorithm for Feedback and Feedforward Policies towards Robot Control Robust to Sensing Failures—0
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task—0
Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Generalized Reinforcement Learning for Building Control using Behavioral Cloning—0
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation—0
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Benchmark Results

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