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 691–700 of 15113 papers

TitleStatusHype
Active Reinforcement Learning for Robust Building ControlCode1
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Deep Reinforcement Learning in Parameterized Action SpaceCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learningCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
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Benchmark Results

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