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Deep Generative Prior for First Order Inverse Optimization
Haoyu Yang, Kamyar Azizzadenesheli, Haoxing Ren
Inverse design optimization aims to infer system parameters from observed solutions, posing critical challenges across domains such as semiconductor manufacturing, structural engin…
Efficient Exploration through Bayesian Deep Q-Networks
Kamyar Azizzadenesheli, Animashree Anandkumar
We study reinforcement learning (RL) in high dimensional episodic Markov decision processes (MDP). We consider value-based RL when the optimal Q-value is a linear function of d-dim…
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have be…
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically wit…
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Planning plays an important role in the broad class of decision theory. Planning has drawn much attention in recent work in the robotics and sequential decision making areas. Recen…
Reinforcement Learning of POMDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have be…