Search Results for author:
Found 9 papers, 5 papers with code
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."
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree.
Lookahead-Bounded Q-Learning
We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably convergent variant of Q-learning that seeks to improve the performance of standard Q-learning in stochastic environments through the use of ``lookahead'' upper and lower bounds.
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.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
Feedback-Based Tree Search for Reinforcement Learning
Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process.
Model-based Reinforcement Learning
reinforcement-learning
+1
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds
Monte Carlo Tree Search (MCTS), most famously used in game-play artificial intelligence (e. g., the game of Go), is a well-known strategy for constructing approximate solutions to sequential decision problems.
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures
In this paper, we consider a finite-horizon Markov decision process (MDP) for which the objective at each stage is to minimize a quantile-based risk measure (QBRM) of the sequence of future costs; we call the overall objective a dynamic quantile-based risk measure (DQBRM).
