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 9601–9650 of 15113 papers

TitleStatusHype
Learning to Deceive Knowledge Graph Augmented Models via Targeted PerturbationCode0
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning—0
Stochastic Inverse Reinforcement Learning—0
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning—0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
Learning Guidance Rewards with Trajectory-space SmoothingCode1
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement LearningCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement LearningCode1
Option Hedging with Risk Averse Reinforcement Learning—0
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning—0
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning—0
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Sample Efficient Reinforcement Learning with REINFORCE—0
Adversarial Attacks on Deep Algorithmic Trading Policies—0
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based GamesCode0
Incorporating Stylistic Lexical Preferences in Generative Language Models—0
Detecting Rewards Deterioration in Episodic Reinforcement LearningCode0
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
Error Bounds of Imitating Policies and Environments—0
CoinDICE: Off-Policy Confidence Interval Estimation—0
Accelerating Reinforcement Learning with Learned Skill PriorsCode1
What are the Statistical Limits of Offline RL with Linear Function Approximation?—0
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments—0
Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement LearningCode1
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
Logistic Q-Learning—0
Safety Verification of Model Based Reinforcement Learning Controllers—0
PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text GenerationCode1
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
Improving Generalization in Reinforcement Learning with Mixture RegularizationCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Multi-Radar Tracking Optimization for Collaborative Combat—0
Quality of service based radar resource management using deep reinforcement learning—0
Runtime Safety Assurance Using Reinforcement Learning—0
Negotiating Team Formation Using Deep Reinforcement Learning—0
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification—0
Reinforcement Learning for Optimization of COVID-19 Mitigation policiesCode1
Iterative Amortized Policy OptimizationCode1
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
A case for new neural networks smoothness constraints—0
Dream and Search to Control: Latent Space Planning for Continuous ControlCode1
Learning by Competition of Self-Interested Reinforcement Learning AgentsCode0
Imitation with Neural Density Models—0
Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction SequencesCode2
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous DrivingCode2
Model-based Policy Optimization with Unsupervised Model AdaptationCode1
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Benchmark Results

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