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 781–790 of 15113 papers

TitleStatusHype
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUsCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Optimal Goal-Reaching Reinforcement Learning via Quasimetric LearningCode1
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentsCode1
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Inverse Reinforcement Learning without Reinforcement LearningCode1
Optimal Transport for Offline Imitation LearningCode1
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Benchmark Results

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