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 10001–10050 of 15113 papers

TitleStatusHype
Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems—0
Distributed Deep Reinforcement Learning: An Overview—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
On the Convergence of Reinforcement Learning in Nonlinear Continuous State Space Problems—0
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks—0
MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty—0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning—0
Deep reinforcement learning for feedback control in a collective flashing ratchetCode0
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space—0
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning—0
Provable Multi-Objective Reinforcement Learning with Generative Models—0
Online Model Selection for Reinforcement Learning with Function Approximation—0
Weighted Entropy Modification for Soft Actor-Critic—0
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband—0
Inverse Reinforcement Learning via Matching of Optimality Profiles—0
Experimental Study on Reinforcement Learning-based Control of an Acrobot—0
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks—0
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks—0
Leveraging the Variance of Return Sequences for Exploration Policy—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell—0
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data—0
Fault-Aware Robust Control via Adversarial Reinforcement Learning—0
Modality-Buffet for Real-Time Object Detection—0
Reinforcement Learning of Graph Neural Networks for Service Function Chaining—0
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
REALab: An Embedded Perspective on Tampering—0
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning—0
Towards a General Framework for ML-based Self-tuning Databases—0
Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning—0
Towards Learning Controllable Representations of Physical Systems—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Blind Decision Making: Reinforcement Learning with Delayed Observations—0
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning—0
Constrained Model-Free Reinforcement Learning for Process Optimization—0
ACDER: Augmented Curiosity-Driven Experience Replay—0
Analog Circuit Design with Dyna-Style Reinforcement Learning—0
Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power SystemsCode0
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing—0
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation—0
RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems—0
A Geometric Perspective on Self-Supervised Policy Adaptation—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning—0
Show:102550
← PrevPage 201 of 303Next →

Benchmark Results

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