Created
April 27, 2016 02:34
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| This repo implements recurrent neural network that optimizes TRPO loss function. Moreover, we use | |
| a neural network as value function. | |
| https://github.com/wojzaremba/trpo_rnn , commit_id da6fb44bd2980cd26dd057aff01f55a533a742fa | |
| Execute run.py to start 4 sessions of screen that reproduce results on: "Copy-v0", "DuplicatedInput-v0", | |
| "ReversedAddition-v0", "ReversedAddition3-v0" |
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How do you represent your state? Given that the environment returns Discrete(N) observations, I assume you are concatenating these observations and maybe bundle them with the most recent action? I am working on solving the environment and would appreciate learning about your approach/experimentation.