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 3791–3800 of 15113 papers

TitleStatusHype
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty—0
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue—0
Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning—0
Resource Optimization for Tail-Based Control in Wireless Networked Control Systems—0
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
Revealing the learning process in reinforcement learning agents through attention-oriented metrics—0
Beyond Optimism: Exploration With Partially Observable RewardsCode0
Equivariant Offline Reinforcement Learning—0
Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned PoliciesCode0
Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyond—0
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Benchmark Results

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