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 1531–1540 of 15113 papers

TitleStatusHype
Personalisation via Dynamic Policy Fusion—0
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning—0
Focus On What Matters: Separated Models For Visual-Based RL Generalization—0
Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning—0
Grounded Curriculum Learning—0
Constrained Reinforcement Learning for Safe Heat Pump ControlCode0
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization—0
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning—0
Strongly-polynomial time and validation analysis of policy gradient methods—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
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Benchmark Results

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