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PyTorch implementation of Episodic Meta Reinforcement Learning on variants of the "Two-Step" task. Reproduces the results found in three papers. Check the ReadMe for more details!

License: GNU General Public License v3.0

Python 100.00%
meta-learning meta-reinforcement-learning reinforcement-learning deepmind neuroscience two-step-task episodic-memory differentiable-neural-dictionary lstm a2c

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meta-rl-twostep-task's Issues

A question about TwoStepTask.step() function

Thanks for providing such a readable code.

If I am correct, you were compressing stage1 and stage2 into 1 trial. In this case, I think the next_state of this trial should be S1, because each trial should begin with stage1. However, in my simple simulation, the non-ep environment never returns S1 [ 1, 0,0]:
image

I wonder if it is a special design for the "incremental" case? I am not sure if I am on the same page with you. Can you offer a brief definition of "incremental", "episodic" in your README?

Thank you very much!!

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