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 2301–2325 of 15113 papers

TitleStatusHype
Causality from Bottom to Top: A Survey—0
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting—0
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC—0
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency—0
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Neural-Kernel Conditional Mean Embeddings—0
Horizon-Free Regret for Linear Markov Decision Processes—0
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement LearningCode0
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning—0
Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis—0
TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement LearningCode0
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban EnvironmentsCode2
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback—0
Learning to Describe for Predicting Zero-shot Drug-Drug InteractionsCode0
Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter ControlCode0
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware PerspectiveCode0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
(N,K)-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model—0
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
Show:102550
← PrevPage 93 of 605Next →

Benchmark Results

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