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

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Showing 4301–4350 of 15113 papers

TitleStatusHype
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions—0
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning—0
Ad Headline Generation using Self-Critical Masked Language Model—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms—0
A Digital Twin Framework for Reinforcement Learning with Real-Time Self-Improvement via Human Assistive Teleoperation—0
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning—0
Meta-Reinforcement Learning with Self-Modifying Networks—0
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning—0
A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning—0
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning—0
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
A Distributional View on Multi-Objective Policy Optimization—0
A distributional view on multi objective policy optimization—0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning—0
Adjacency constraint for efficient hierarchical reinforcement learning—0
Accelerating Distributed Deep Reinforcement Learning by In-Network Experience Sampling—0
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning—0
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms—0
A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning—0
A dual mode adaptive basal-bolus advisor based on reinforcement learning—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Advanced Scaling Methods for VNF deployment with Reinforcement Learning—0
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Advances in Preference-based Reinforcement Learning: A Review—0
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning—0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Advancing Renewable Electricity Consumption With Reinforcement Learning—0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective—0
Advantage Amplification in Slowly Evolving Latent-State Environments—0
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning—0
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Adversarial Attacks on Deep Algorithmic Trading Policies—0
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
Adversarial Environment Design via Regret-Guided Diffusion Models—0
Adversarial Exploitation of Policy Imitation—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
Adversarial Imitation Learning On Aggregated Data—0
Adversarial Imitation Learning via Random Search—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning—0
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Benchmark Results

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