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 11551–11600 of 15113 papers

TitleStatusHype
Why Online Reinforcement Learning is Causal—0
Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification—0
Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters.—0
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters—0
Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model—0
Wield: Systematic Reinforcement Learning With Progressive Randomization—0
Will it Blend? Composing Value Functions in Reinforcement Learning—0
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach—0
Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning—0
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance—0
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic—0
Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence—0
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving—0
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation—0
Words as Beacons: Guiding RL Agents with High-Level Language Prompts—0
Workflow-Guided Response Generation for Task-Oriented Dialogue—0
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks—0
World Models Increase Autonomy in Reinforcement Learning—0
World of Bits: An Open-Domain Platform for Web-Based Agents—0
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces—0
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning—0
Worm-level Control through Search-based Reinforcement Learning—0
Worst-Case Regret Bounds for Exploration via Randomized Value Functions—0
Worst Cases Policy Gradients—0
X-MEN: Guaranteed XOR-Maximum Entropy Constrained Inverse Reinforcement Learning—0
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
Yes, Q-learning Helps Offline In-Context RL—0
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL—0
You Only Live Once: Single-Life Reinforcement Learning—0
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization—0
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning—0
Zero-Shot Action Generalization with Limited Observations—0
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation—0
Zero Shot Learning on Simulated Robots—0
Zero-Shot Policy Transfer with Disentangled Attention—0
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning—0
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation—0
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty—0
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks—0
Zero-Shot Reward Specification via Grounded Natural Language—0
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs—0
Zero-shot Text Classification via Reinforced Self-training—0
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning—0
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer—0
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization—0
Zeroth-Order Supervised Policy Improvement—0
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning—0
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Benchmark Results

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