Abstract
In this chapter, we will discuss some of the resources available for building one’s own reinforcement learning agent easily, or implementing one with the least amount of code. We will also cover some standardized environment, platforms, and community boards against which one can evaluate their custom agent’s performances on different types of reinforcement learning tasks and challenges.
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© 2019 Springer Nature Singapore Pte Ltd.
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Sewak, M. (2019). Implementation Resources. In: Deep Reinforcement Learning. Springer, Singapore. https://doi.org/10.1007/978-981-13-8285-7_7
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DOI: https://doi.org/10.1007/978-981-13-8285-7_7
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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-13-8285-7
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