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Evolution Strategies and Genetic Algorithms Policy in DRL #459
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Hi @bahaTRKGLU I have looked at evolutionary methods a little, but the main challenge is the API. This means for any implementation, it needs to obey the agent API. A simple example that's non-gradient is the random agent. If you're able to make evolutionary method conform to this API then u can directly plug and play it in the lab. Interested to see if you have a design in mind! |
I basically want to implement the code shared by uber and compare the results with the algorithms in SLM-lab. But I'm a rookie in this regard and I couldn't. |
Unfortunately there's no plan to do so and we the authors are quite occupied, but I'll mark this as help wanted for anyone who wishes to take it on. |
thank you so much for nice job.
I want to implement one of the algorithms without gradient in this project and compare the results with the algorithms in this project such as actorcritic, dqn ,reinforce.
I have a code that works in Pytorch https://towardsdatascience.com/reinforcement-learning-without-gradients-evolving-agents-using-genetic-algorithms-8685817d84f.
Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
https://arxiv.org/pdf/1712.06567.pdf
Evolution Strategies as a Scalable Alternative to Reinforcement Learning https://arxiv.org/pdf/1703.03864.pdf
How can I do the implementation?
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