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Fix a broken link. #418

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4 changes: 1 addition & 3 deletions docs/spinningup/rl_intro3.rst
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Expand Up @@ -338,9 +338,7 @@ is called the **reward-to-go** from that point, and this policy gradient express

**But how is this better?** A key problem with policy gradients is how many sample trajectories are needed to get a low-variance sample estimate for them. The formula we started with included terms for reinforcing actions proportional to past rewards, all of which had zero mean, but nonzero variance: as a result, they would just add noise to sample estimates of the policy gradient. By removing them, we reduce the number of sample trajectories needed.

An (optional) proof of this claim can be found `here`_, and it ultimately depends on the EGLP lemma.

.. _`here`: ../spinningup/extra_pg_proof1.html
An (optional) proof of this claim can be found `here <../spinningup/extra_pg_proof1.html>`_, and it ultimately depends on the EGLP lemma.

Implementing Reward-to-Go Policy Gradient
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