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 941–950 of 15113 papers

TitleStatusHype
Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning—0
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
Smell of Source: Learning-Based Odor Source Localization with Molecular Communication—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
A view on learning robust goal-conditioned value functions: Interplay between RL and MPCCode0
Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures—0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning—0
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization—0
Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits—0
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Benchmark Results

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