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 6726–6750 of 15113 papers

TitleStatusHype
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning—0
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning—0
Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
A Dynamic Penalty Function Approach for Constraints-Handling in Reinforcement Learning—0
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting—0
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning—0
A General Framework on Enhancing Portfolio Management with Reinforcement Learning—0
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experiment—0
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning—0
Dynamic probabilistic logic models for effective abstractions in RL—0
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning—0
Dynamic Regret of Policy Optimization in Non-stationary Environments—0
Dynamic Reinforcement Learning for Actors—0
Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network—0
Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning—0
Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Dynamic Sampling that Adapts: Iterative DPO for Self-Aware Mathematical Reasoning—0
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning—0
Dynamic Shielding for Reinforcement Learning in Black-Box Environments—0
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning—0
Dynamic Temporal Reconciliation by Reinforcement learning—0
Show:102550
← PrevPage 270 of 605Next →

Benchmark Results

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