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 6476–6500 of 15113 papers

TitleStatusHype
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach—0
The Case for Automatic Database Administration using Deep Reinforcement Learning—0
The Central Role of the Loss Function in Reinforcement Learning—0
The Challenges of Exploration for Offline Reinforcement Learning—0
The Complexity of Markov Equilibrium in Stochastic Games—0
The Complex Negotiation Dialogue Game—0
The Concept of Criticality in Reinforcement Learning—0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning—0
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model—0
The Differences Between Direct Alignment Algorithms are a Blur—0
The Difficulty of Passive Learning in Deep Reinforcement Learning—0
The Ecosystem Path to General AI—0
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning—0
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy—0
The effects of negative adaptation in Model-Agnostic Meta-Learning—0
The Eigenoption-Critic Framework—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits—0
The Essential Elements of Offline RL via Supervised Learning—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting—0
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Benchmark Results

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