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 2801–2810 of 15113 papers

TitleStatusHype
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents—0
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity—0
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement LearningCode1
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning—0
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of SkillsCode1
Behavior Alignment via Reward Function Optimization—0
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Benchmark Results

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