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 981–990 of 15113 papers

TitleStatusHype
The Differences Between Direct Alignment Algorithms are a Blur—0
Reinforcement Learning with Segment Feedback—0
Reinforcement Learning for Long-Horizon Interactive LLM Agents—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning—0
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning—0
Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer—0
Recursive generalized type-2 fuzzy radial basis function neural networks for joint position estimation and adaptive EMG-based impedance control of lower limb exoskeletonsCode0
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Benchmark Results

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