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 3031–3040 of 15113 papers

TitleStatusHype
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
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—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
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms—0
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs—0
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
O-MAPL: Offline Multi-agent Preference Learning—0
Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks—0
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Benchmark Results

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