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Proximal Policy Optimization, A2C style agent that can interact with StarCraft: Brood War environment scenarios. Uses SAIDA tools as a framework, and Keras/Tensorflow. Written primarily in Python. Last edited April 25, 2020.

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Senior_Project_Repository

Project created by Ben Harruff for Purdue University - Fort Wayne

Proximal Policy Optimization, A2C style agent that can interact with StarCraft: Brood War environment scenarios. Uses SAIDA tools as a framework, and Keras/Tensorflow. Written in Python. Last edited April 25, 2020.

Files of Note:

python\saida_agent_example\avoidReaver\avoid_reavers_PPO_Ben_v4.py

  • main file that runs. Keeps track of hyperparameters.

python\core\algorithm\PPO.py

References: [1] M. Čertický and D. Churchill, "The Current State of StarCraft AI Competitions and Bots," In Proceedings of the AIIDE 2017 Workshop on Artificial Intelligence for Strategy Games, 2017. [2] M. Čertický, D. Churchill, K.-J. Kim, M. Čertický and R. Kelly, "StarCraft AI Competitions, Bots and Tournament Manager Software," IEEE Transactions on Games (ToG), vol. 1, no. 13, p. 1, 2018. [3] D. Churchill, M. Preuss, F. Richoux, G. Synnaeve, A. Uriarte, S. Ontanón and M. Čertický, "Starcraft Bots and Competitions," in Encyclopedia of Computer Graphics and Games (ECGG), Springer International Publishing, 2016. [4] TeamSAIDA, "SAIDA_RL," GitHub repository, 2019. [5] V. Mnih et al., "Playing Atari with Deep Reinforcement Learning," DeepMind Technologies. [6] J. Schulman, F. Wolski, P. Dhariwal, A. Radford and O. Klimov, "Proximal Policy Optimization Algorithms," CoRR, vol. abs/1707.06347, 2017. [7] L. Hardesty, "Explained: Neural Networks," MIT News Office, 2017. [8] AurelianTactics, "PPO Hyperparameters and Ranges," 25 July 2018. [Online]. Available: https://medium.com/aureliantactics/ppo-hyperparameters-and-ranges-6fc2d29bccbe. [Accessed 6 April 2020]. [9] S. John et al., "Trust Region Policy Optimization," University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, no. https://arxiv.org/pdf/1502.05477.pdf, 2017. [10] Keras Team, "Keras: The Python Deep Learning Library," GitHub repository, 2019. [11] N. S. Keskar and R. Socher, "Improving Generalization Performance by Switching from Adam to SGD," Salesforce Research, no. https://arxiv.org/pdf/1712.07628.pdf, 2017.

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Proximal Policy Optimization, A2C style agent that can interact with StarCraft: Brood War environment scenarios. Uses SAIDA tools as a framework, and Keras/Tensorflow. Written primarily in Python. Last edited April 25, 2020.

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