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 631–640 of 15113 papers

TitleStatusHype
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio ApplicationsCode1
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
Active Reinforcement Learning for Robust Building ControlCode1
The Effective Horizon Explains Deep RL Performance in Stochastic EnvironmentsCode1
World Models via Policy-Guided Trajectory DiffusionCode1
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL ApproachCode1
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Benchmark Results

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