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 2726–2750 of 15113 papers

TitleStatusHype
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption—0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
Towards Optimal Differentially Private Regret Bounds in Linear MDPs—0
Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion—0
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and ChallengesCode0
Deep Distributional Learning with Non-crossing Quantile Network—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems—0
RL-based Control of UAS Subject to Significant Disturbance—0
Fast Adaptation with Behavioral Foundation Models—0
Boosting Universal LLM Reward Design through the Heuristic Reward Observation Space Evolution—0
Better Decisions through the Right Causal World Model—0
Trust-Region Twisted Policy ImprovementCode0
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems—0
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models—0
Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems—0
TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning—0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning—0
The Role of Environment Access in Agnostic Reinforcement Learning—0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents—0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making—0
Learning Dual-Arm Coordination for Grasping Large Flat Objects—0
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
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Benchmark Results

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