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 2631–2640 of 15113 papers

TitleStatusHype
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Active search and coverage using point-cloud reinforcement learning—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Challenges for Reinforcement Learning in Quantum Circuit DesignCode1
Learning to Act without ActionsCode1
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory OptimizationCode1
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
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Benchmark Results

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