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 7776–7800 of 15113 papers

TitleStatusHype
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