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 5101–5125 of 15113 papers

TitleStatusHype
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems—0
HoME: a Household Multimodal Environment—0
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat—0
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform—0
Horizon-Free Regret for Linear Markov Decision Processes—0
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes—0
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies—0
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs—0
Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
How Can Creativity Occur in Multi-Agent Systems?—0
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study—0
How does AI play football? An analysis of RL and real-world football strategies—0
How does the structure embedded in learning policy affect learning quadruped locomotion?—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
How do Offline Measures for Exploration in Reinforcement Learning behave?—0
How hard is my MDP?" The distribution-norm to the rescue"—0
How many weights are enough : can tensor factorization learn efficient policies ?—0
How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning—0
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?—0
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning—0
How to Combine Tree-Search Methods in Reinforcement Learning—0
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies—0
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Benchmark Results

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