This repository presents a custom implementation of game in the paper A multi-agent reinforcement learning model of common-pool resource appropriation by Deep Mind, presented at Advances in Neural Information Processing Systems 30 (NIPS 2017).
The main goal of the paper is to introduce Reinforcement Learning based simulated environments as a way of addressing the modeling of common-pool resource dynamics. This, because abstract models based on non-cooperative game theory fail to predict deal world dynamics of these scenarios.
In this implementation we replaced the original DQN algorithm of the original paper with DDQN with replay buffer. Furthermore, we dont use a MLP architecture but rather a light weight CNN that could theoretically process the agents observations easily. More information on the implementation can be found in the following example Colab.
See details in Google Colab
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View source on GitHub
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The only requirements of the project are
Additionally, if you want to go ahead and see the code functionality first, you can check the example Colab notebook.
- Use and compare novel RL value-based and policy-gradient based algorithms.
This repository is based on the code of: