scholarly journals Reinforcement Learning for Bio-Retrosynthesis

2019 ◽  
Author(s):  
Mathilde Koch ◽  
Thomas Duigou ◽  
Jean-Loup Faulon
Keyword(s):  
Reinforcement Learning ◽  
Metabolic Engineering ◽  
Development Process ◽  
Computational Design ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Command Line Tool ◽  
Living Organisms ◽  
Media Supplements ◽  
Engineering Projects

AbstractMetabolic engineering aims to produce chemicals of interest from living organisms, to advance towards greener chemistry. Despite efforts, the research and development process is still long and costly and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bio-retrosynthesis space using an Artificial Intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden dataset of 20 manually curated experimental pathways as well as on a larger dataset of 152 successful metabolic engineering projects. Moreover, we provide a novel feature, that suggests potential media supplements to complement the enzymatic synthesis plan.

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

scholarly journals Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning

2020 ◽  
Vol 11 (40) ◽  
pp. 10959-10972
Author(s):  
Xiaoxue Wang ◽  
Yujie Qian ◽  
Hanyu Gao ◽  
Connor W. Coley ◽  
Yiming Mo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Value Network ◽  
Synthesis Routes

A new MCTS variant with a reinforcement learning value network and solvent prediction model proposes shorter synthesis routes with greener solvents.

scholarly journals Predicting Game Difficulty and Engagement Using AI Players

2021 ◽  
Vol 5 (CHI PLAY) ◽  
pp. 1-17
Author(s):  
Shaghayegh Roohi ◽  
Christian Guckelsberger ◽  
Asko Relas ◽  
Henri Heiskanen ◽  
Jari Takatalo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Accuracy ◽  
Selection Strategy ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Average Performance ◽  
Novel Approach ◽  
Player Modelling ◽  
Human Player

This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. We have previously demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.

scholarly journals Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search

2013 ◽  
Vol 48 ◽  
pp. 841-883 ◽  
Author(s):  
A. Guez ◽  
D. Silver ◽  
P. Dayan
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Search Space ◽  
Search Tree ◽  
Benchmark Problems ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
The Face ◽  
Almost All ◽  
Infinite State

Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, planning optimally in the face of uncertainty is notoriously taxing, since the search space is enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach avoids expensive applications of Bayes rule within the search tree by sampling models from current beliefs, and furthermore performs this sampling in a lazy manner. This enables it to outperform previous Bayesian model-based reinforcement learning algorithms by a significant margin on several well-known benchmark problems. As we show, our approach can even work in problems with an infinite state space that lie qualitatively out of reach of almost all previous work in Bayesian exploration.

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