Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Author(s): Sherwin Chen

Originally published on Towards AI.

Beyond Hierarchical Reinforcement learning with Off-policy correction(HIRO)

This is the second post of the series, in which we will talk about a novel Hierarchical Reinforcement Learning built upon HIerarchical Reinforcement learning with Off-policy correction(HIRO) we discussed in the previous post.

This post is comprised of two sections. In the first section, we first compared architectures of representation learning for HRL and HIRO; then we started from Claim 4 in the paper, seeing how to learn good representations that lead to bounded sub-optimality and how the intrinsic reward for the low-level policy is defined; we will provide the pseudocode for the algorithm at the end of this section. In… Read the full blog for free on Medium.

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Published via Towards AI