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 3926–3950 of 15113 papers

TitleStatusHype
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
Constrained Ensemble Exploration for Unsupervised Skill Discovery—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Embedding-Aligned Language Models—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Model-free reinforcement learning with noisy actions for automated experimental control in opticsCode0
Offline Reinforcement Learning from Datasets with Structured Non-StationarityCode0
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality—0
Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of ExperiencesCode0
Variational Delayed Policy OptimizationCode0
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
A finite time analysis of distributed Q-learning—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks—0
Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Learning to sample fibers for goodness-of-fit testing—0
Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systemsCode0
Leader Reward for POMO-Based Neural Combinatorial Optimization—0
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model—0
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Benchmark Results

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