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Found 11 papers, 2 papers with code
Towards Generalized Inverse Reinforcement Learning
This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal.
Data-Centric Financial Large Language Models
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes.
Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information
In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells.
Faster Approximate Dynamic Programming by Freezing Slow States
We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly."
Eigen mode selection in human subject game experiment
Eigen mode selection ought to be a practical issue in some real game systems, as it is a practical issue in the dynamics behaviour of a building, bridge, or molecular, because of the mathematical similarity in theory.
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
Inverse Multiobjective Optimization Through Online Learning
We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions.
Dynamic Subgoal-based Exploration via Bayesian Optimization
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration.
Using experimental game theory to transit human values to ethical AI
Knowing the reflection of game theory and ethics, we develop a mathematical representation to bridge the gap between the concepts in moral philosophy (e. g., Kantian and Utilitarian) and AI ethics industry technology standard (e. g., IEEE P7000 standard series for Ethical AI).
