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SOTA -> state of the art #90

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Jun 28, 2019
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SOTA -> state of the art #90

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2 changes: 1 addition & 1 deletion docs/user/algorithms.rst
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Expand Up @@ -31,7 +31,7 @@ They are all implemented with `MLP`_ (non-recurrent) actor-critics, making them
Why These Algorithms?
=====================

We chose the core deep RL algorithms in this package to reflect useful progressions of ideas from the recent history of the field, culminating in two algorithms in particular---PPO and SAC---which are close to SOTA on reliability and sample efficiency among policy-learning algorithms. They also expose some of the trade-offs that get made in designing and using algorithms in deep RL.
We chose the core deep RL algorithms in this package to reflect useful progressions of ideas from the recent history of the field, culminating in two algorithms in particular---PPO and SAC---which are close to state of the art on reliability and sample efficiency among policy-learning algorithms. They also expose some of the trade-offs that get made in designing and using algorithms in deep RL.

The On-Policy Algorithms
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