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 3961–3970 of 15113 papers

TitleStatusHype
Reinforcement learning-based estimation for partial differential equations—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Generative Slate Recommendation with Reinforcement Learning—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning—0
Generalization through Diversity: Improving Unsupervised Environment Design—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Domain-adapted Learning and Interpretability: DRL for Gas Trading—0
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient—0
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Benchmark Results

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