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 2731–2740 of 15113 papers

TitleStatusHype
CubeTR: Learning to Solve The Rubiks Cube Using Transformers—0
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback—0
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment—0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things—0
Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning—0
Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning—0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints—0
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning—0
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning—0
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Benchmark Results

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