Vol 15, No1: 2019:50-57

Improvement of Deep Reinforcement Learning Using Curriculum in Game Environment

Mohammadreza Mohammadnejad, Morteza Dorrigiv, Farzin Yaghmaee

DOI: 10.22122/jrrs.v15i1.3446


Introduction: Training deep curriculum learning is a kind of smart agent training in which, first the simple acts, and then, the difficult acts are trained to smart agent. In this study, we proposed a new framework for training deep curriculum learning to defense-based game in particular Dragon Cave.

Materials and Methods: Deep reinforcement learning approach with curriculum learning was used to train an intelligent agent in the game Dragon Cave. Curriculum learning paradigm started from simple tasks, and then gradually tried harder ones. Using Proximal Policy Optimization, the intelligent agents were trained in various environments, once in a curriculum-learning environment, and once in an environment without curriculum learning. Then, they started the game in the same environment.

Results: The improvement of the agent was observed with deep curriculum reinforcement learning.

Conclusion: It seems that the deep curriculum reinforcement learning increases the rate and the quality of intelligent agent training in complex environment of strategic games.


Intelligent agent; Deep reinforcement with curriculum learning; Machine learning; Neural network

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