Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (3): 666-674.doi: 10.13229/j.cnki.jdxbgxb20200842

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Deep reinforcement learning model for text games

Yong LIU(),Lei XU,Chu-han ZHANG   

  1. School of Computer Science and Technology，Heilongjiang University，Harbin 150080，China
  • Received:2020-11-03 Online:2022-03-01 Published:2022-03-08

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Abstract:

In order to improve the performance of agents in text games， a deep reinforcement learning model called SADSR based on siamese network and deep successor representation was proposed. Firstly， the model uses natural language processing technology to process text information to obtain the embedding vector of words， which can effectively transform the text information into digital vector. Then the siamese network is used to extract the features of state and action information， and the extracted state feature vector is used to predict the immediate reward， and the joint vector of state and action is used to predict the successor representation. Finally， the action value is calculated by the interaction function between the successor representation and the weight vector of the specific layer. The experimental results show that the model can effectively fit the value function. Compared with the current mainstream models， SADSR can improve the performance of agents in text games by 10%~60%.

Key words: artificial intelligence, text games, deep reinforcement learning, siamese network, deep successor representation

CLC Number: 

  • TP181

Fig.1

SADSR model architecture"

Fig.2

Text representation"

Fig.3

Sum-tree"

Table 1

Statistical data and experimental results of games on Pyfiction platform"

项目类别实验环境
Saving JohnMachine of Death
StatisticsVocab size≥1119≥2055
Action vocab size≥168≥399
State transitionsDeterministicStochastic
Endings≥5≥15
Optimal final score≥19.4≥28.6
Final RewardRANDOM-11.12（±0.52）-7.26（±12.61）
DRRN1.84（±16.56）11.50（±19.05）
SSAQN19.40（±0.00）12.20（±13.04）
SADSR19.40（±0.00）20.06（±4.70）

Fig.4

Agent learning process in Pyfiction platform"

Table 2

Statistical data and experimental results of games on Jericho platform"

项目类别游戏名称
inhumaneludicorppentari
Statisticstemplets141187155
words409503472
MaxScore30015070
Final RewardRANDOM013.20
NAIL0.68.40
TDQN0.7617.4
DRRN013.827.2
SADSR18（±4.47）16.8（±0.45）30（±0.0）

Fig.5

Agent learning process in Jericho platform"

Fig.6

Agent learning process in inhumane"

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