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 11251–11275 of 15113 papers

TitleStatusHype
Gym-saturation: an OpenAI Gym environment for saturation provers—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps—0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning—0
Hacking Google reCAPTCHA v3 using Reinforcement Learning—0
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning—0
Halftoning with Multi-Agent Deep Reinforcement Learning—0
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization—0
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models—0
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control—0
Hamiltonian Policy Optimization—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
Handling Cold-Start Collaborative Filtering with Reinforcement Learning—0
Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control—0
Hand-Object Interaction Pretraining from Videos—0
Hard instance learning for quantum adiabatic prime factorization—0
Hardness in Markov Decision Processes: Theory and Practice—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
Hardware Trojan Insertion Using Reinforcement Learning—0
HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
Harnessing Causality in Reinforcement Learning With Bagged Decision Times—0
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Benchmark Results

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