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 1591–1600 of 15113 papers

TitleStatusHype
Robust Reinforcement Learning with Dynamic Distortion Risk MeasuresCode0
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies—0
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning—0
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema—0
KAN v.s. MLP for Offline Reinforcement Learning—0
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
Quasimetric Value Functions with Dense Rewards—0
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Benchmark Results

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