Self Organizing Decision Tree Based on Reinforcement Learning and its Application on State Space Partition

2006 ◽  
Author(s):  
Kao-Shing Hwang ◽  
Tsung-Wen Yang ◽  
Chia-Ju Lin
Keyword(s):  
Reinforcement Learning ◽  
Decision Tree ◽  
State Space ◽  
Space Partition ◽  
Self Organizing

scholarly journals Playing Card-Based RTS Games with Deep Reinforcement Learning

2019 ◽  
Author(s):  
Tianyu Liu ◽  
Zijie Zheng ◽  
Hongchang Li ◽  
Kaigui Bian ◽  
Lingyang Song
Keyword(s):  
Reinforcement Learning ◽  
Decision Tree ◽  
State Space ◽  
Imperfect Information ◽  
Rule Based ◽  
Playing Card ◽  
Board Games ◽  
Deep Model ◽  
Game Ai ◽  
Rts Game

Game AI is of great importance as games are simulations of reality. Recent research on game AI has shown much progress in various kinds of games, such as console games, board games and MOBA games. However, the exploration in RTS games remains a challenge for their huge state space, imperfect information, sparse rewards and various strategies. Besides, the typical card-based RTS games have complex card features and are still lacking solutions. We present a deep model SEAT (selection-attention) to play card-based RTS games. The SEAT model includes two parts, a selection part for card choice and an attention part for card usage, and it learns from scratch via deep reinforcement learning. Comprehensive experiments are performed on Clash Royale, a popular mobile card-based RTS game. Empirical results show that the SEAT model agent makes it to reach a high winning rate against rule-based agents and decision-tree-based agent.

Visualization of Learning Process in “State and Action” Space Using Self-Organizing Maps

2016 ◽  
Vol 20 (6) ◽  
pp. 983-991
Author(s):  
Akira Notsu ◽  
◽  
Yuichi Hattori ◽  
Seiki Ubukata ◽  
Katsuhiro Honda ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Learning Process ◽  
Numerical Experiments ◽  
Action Learning ◽  
Human Learning ◽  
Search Problem ◽  
Self Organizing Maps ◽  
Learning Agents ◽  
Self Organizing

In reinforcement learning, agents can learn appropriate actions for each situation based on the consequences of these actions after interacting with the environment. Reinforcement learning is compatible with self-organizing maps that accomplish unsupervised learning by reacting to impulses and strengthening neurons. Therefore, numerous studies have investigated the topic of reinforcement learning in which agents learn the state space using self-organizing maps. In this study, while we intended to apply these previous studies to transfer the learning and visualization of the human learning process, we introduced self-organizing maps into reinforcement learning and attempted to make their “state and action” learning process visible. We performed numerical experiments with the 2D goal-search problem; our model visualized the learning process of the agent.

scholarly journals Reinforcement learning versus swarm intelligence for autonomous multi-HAPS coordination

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ogbonnaya Anicho ◽  
Philip B. Charlesworth ◽  
Gurvinder S. Baicher ◽  
Atulya K. Nagar
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Swarm Intelligence ◽  
Performance Indicators ◽  
Convergence Rates ◽  
Tuning Parameters ◽  
Continuous State Space ◽  
Continuous State ◽  
User Coverage ◽  
Better Than

AbstractThis work analyses the performance of Reinforcement Learning (RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area coverage. It builds upon previous work which looked at various elements of both algorithms. The main aim of this paper is to address the continuous state-space challenge within this work by using partitioning to manage the high dimensionality problem. This enabled comparing the performance of the classical cases of both RL and SI establishing a baseline for future comparisons of improved versions. From previous work, SI was observed to perform better across various key performance indicators. However, after tuning parameters and empirically choosing suitable partitioning ratio for the RL state space, it was observed that the SI algorithm still maintained superior coordination capability by achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. Though the RL technique showed better average peak user coverage, the unpredictable coverage dip was a key weakness, making SI a more suitable algorithm within the context of this work.

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