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 3151–3175 of 15113 papers

TitleStatusHype
Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach—0
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RLCode0
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization—0
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search—0
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities—0
Reinforcement Learning for Motor Control: A Comprehensive Review—0
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning—0
Environment Descriptions for Usability and Generalisation in Reinforcement Learning—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
Subgoal Discovery Using a Free Energy Paradigm and State Aggregations—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—0
Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI—0
Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and PlanningCode0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed—0
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Learning to Generate Research Idea with Dynamic Control—0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues—0
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
Deep reinforcement learning with time-scale invariant memoryCode0
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Benchmark Results

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