Deep Reinforcement Learning for Playing 2.5D Fighting Games

2018-05-05 · Yu-Jhe Li, Hsin-Yu Chang, Yu-Jing Lin, Po-Wei Wu, Yu-Chiang Frank Wang

Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like height or depth of the characters. Moreover, actions in such games typically involve particular sequential action orders, which also makes the network design very difficult. Based on the network of Asynchronous Advantage Actor-Critic (A3C), we create an OpenAI-gym-like gaming environment with the game of Little Fighter 2 (LF2), and present a novel A3C+ network for learning RL agents. The introduced model includes a Recurrent Info network, which utilizes game-related info features with recurrent layers to observe combo skills for fighting. In the experiments, we consider LF2 in different settings, which successfully demonstrates the use of our proposed model for learning 2.5D fighting games.

📄 PDF Abstract BibTeX arXiv:1805.02070

Code (4)

elvisyjlin/lf2gym 공식 구현

JIElite/AI-research-papers

TobiKick/DL_LF2 tf

acht7111020/DRL-playing-2.5D-fighting-game tf

Tasks

Deep Reinforcement LearningOpenAI Gym reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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