Search Results for author:
Found 10 papers, 2 papers with code
AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback
Large Language Models (LLMs) have demonstrated significant success across various domains.
Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
COREP primarily employs a guided updating mechanism to learn a stable graph representation for states termed as causal-origin representation.
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
We investigate the use of natural language to drive the generalization of policies in multi-agent settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
Model-Based Opponent Modeling
When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before.
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.
Deceptive Opponent Modeling with Proactive Network Interdiction for Stochastic Goal Recognition Control
Goal recognition based on the observations of the behaviors collected online has been used to model some potential applications.
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA).
