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Found 11 papers, 3 papers with code
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process
This paper proposes a two-phase paradigm to aggregate comprehensive information on discrete structures leading to a Discount Markov Diffusion Learnable Kernel (DMDLK).
Robust Imitation Learning from Corrupted Demonstrations
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers.
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control
In this paper, we present a model-based robust RL framework for autonomous greenhouse control to meet the sample efficiency and safety challenges.
MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
MBDP consists of two kinds of dropout mechanisms, where the rollout-dropout aims to improve the robustness with a small cost of sample efficiency, while the model-dropout is designed to compensate for the lost efficiency at a slight expense of robustness.
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics.
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency.
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications.
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference.
Video Motion Segmentation Using New Adaptive Manifold Denoising Model
After that, we employ an embedded manifold denoising approach with the adaptive kernel to segment the motion of rigid and non-rigid objects.
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach
In many graph-based machine learning and data mining approaches, the quality of the graph is critical.
