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 7751–7800 of 15113 papers

TitleStatusHype
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Guiding Robot Exploration in Reinforcement Learning via Automated Planning—0
Guided Exploration in Deep Reinforcement Learning—0
Guided Meta-Policy Search—0
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration—0
Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process—0
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation—0
Guided Safe Shooting: model based reinforcement learning with safety constraints—0
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation—0
Guiding Global Placement With Reinforcement Learning—0
Guiding Reinforcement Learning Exploration Using Natural Language—0
Guiding Representation Learning in Deep Generative Models with Policy Gradients—0
Guiding Safe Exploration with Weakest Preconditions—0
Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education—0
gym-DSSAT: a crop model turned into a Reinforcement Learning environment—0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
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
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
HARPO: Learning to Subvert Online Behavioral Advertising—0
Harvesting energy from turbulent winds with Reinforcement Learning—0
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents—0
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving—0
Hebbian Learning of Bayes Optimal Decisions—0
Hebbian Synaptic Modifications in Spiking Neurons that Learn—0
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Benchmark Results

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