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 4351–4400 of 15113 papers

TitleStatusHype
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning—0
Adversarial joint attacks on legged robots—0
Adversarial Learning of Task-Oriented Neural Dialog Models—0
State-Conditioned Adversarial Subgoal Generation—0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning—0
Adversarially Trained Neural Policies in the Fourier Domain—0
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing—0
Adversarial Model for Offline Reinforcement Learning—0
Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar—0
Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks—0
Adversarial Reinforcement Learning for Procedural Content Generation—0
Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles—0
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control—0
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence—0
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Adversarial Style Transfer for Robust Policy Optimization in Reinforcement Learning—0
Adversarial Text Generation Without Reinforcement Learning—0
Adversarial Training Blocks Generalization in Neural Policies—0
Adversary A3C for Robust Reinforcement Learning—0
Adversary agent reinforcement learning for pursuit-evasion—0
Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop—0
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
A dynamic game approach to training robust deep policies—0
A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn—0
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
Aesthetic Photo Collage with Deep Reinforcement Learning—0
A Fair Federated Learning Framework With Reinforcement Learning—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
A Family of Robust Stochastic Operators for Reinforcement Learning—0
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
Affordance as general value function: A computational model—0
Affordance-based Reinforcement Learning for Urban Driving—0
Affordance-Guided Reinforcement Learning via Visual Prompting—0
A Finite-Sample Analysis of Distributionally Robust Average-Reward Reinforcement Learning—0
A finite time analysis of distributed Q-learning—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
A First-Occupancy Representation for Reinforcement Learning—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
A Framework and Method for Online Inverse Reinforcement Learning—0
A Framework for Constrained and Adaptive Behavior-Based Agents—0
Scaling data-driven robotics with reward sketching and batch reinforcement learning—0
A Framework for dynamically meeting performance objectives on a service mesh—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
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Benchmark Results

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