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 7276–7300 of 15113 papers

TitleStatusHype
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization—0
Approximating a deep reinforcement learning docking agent using linear model trees—0
A Theory of Abstraction in Reinforcement Learning—0
On the Generalization of Representations in Reinforcement LearningCode0
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation—0
Probing the Robustness of Trained Metrics for Conversational Dialogue SystemsCode0
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning—0
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning—0
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming—0
RL-PGO: Reinforcement Learning-based Planar Pose-Graph OptimizationCode0
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite HorizonsCode0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning—0
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach—0
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search—0
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option TemplatesCode0
Context-Hierarchy Inverse Reinforcement Learning—0
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning—0
Reachability analysis in stochastic directed graphs by reinforcement learning—0
Quantum Deep Reinforcement Learning for Robot Navigation TasksCode0
Learning Transferable Reward for Query Object Localization with Policy AdaptationCode0
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks—0
Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing—0
Show:102550
← PrevPage 292 of 605Next →

Benchmark Results

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