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

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Showing 7801–7825 of 15113 papers

TitleStatusHype
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning—0
Hellinger Distance Constrained Regression—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning—0
Heterogeneous Multi-Agent Reinforcement Learning for Unknown Environment Mapping—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Heterotic String Model Building with Monad Bundles and Reinforcement Learning—0
HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments—0
Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method—0
Heuristic-Guided Reinforcement Learning—0
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning—0
Hierarchical and Modular Network on Non-prehensile Manipulation in General Environments—0
Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space—0
Hierarchical Average Reward Policy Gradient Algorithms—0
Hierarchical clustering with deep Q-learning—0
Hierarchical Control of Multi-Agent Systems using Online Reinforcement Learning—0
Hierarchical Decision Making In Electricity Grid Management—0
Hierarchical Decision Transformer—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction—0
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Benchmark Results

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