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 11376–11400 of 15113 papers

TitleStatusHype
Hindsight Curriculum Generation Based Multi-Goal Experience Replay—0
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning—0
Hindsight Generative Adversarial Imitation Learning—0
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning—0
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning—0
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective—0
HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNING—0
Historical Text Normalization with Delayed Rewards—0
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation—0
Lagrangian-based online safe reinforcement learning for state-constrained systems—0
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
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
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Benchmark Results

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