The Benefits of Model-Based Generalization in Reinforcement Learning
- URL: http://arxiv.org/abs/2211.02222v3
- Date: Mon, 10 Jul 2023 16:07:17 GMT
- Title: The Benefits of Model-Based Generalization in Reinforcement Learning
- Authors: Kenny Young, Aditya Ramesh, Louis Kirsch, J\"urgen Schmidhuber
- Abstract summary: Experience Replay (ER) can be considered a simple kind of model, which has proved effective at improving the stability and efficiency of deep RL.
In principle, a learned parametric model could improve on ER by generalizing from real experience to augment the dataset with additional plausible experience.
Here, we provide theoretical and empirical insight into when, and how, we can expect data generated by a learned model to be useful.
- Score: 11.434117284660125
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Model-Based Reinforcement Learning (RL) is widely believed to have the
potential to improve sample efficiency by allowing an agent to synthesize large
amounts of imagined experience. Experience Replay (ER) can be considered a
simple kind of model, which has proved effective at improving the stability and
efficiency of deep RL. In principle, a learned parametric model could improve
on ER by generalizing from real experience to augment the dataset with
additional plausible experience. However, given that learned value functions
can also generalize, it is not immediately obvious why model generalization
should be better. Here, we provide theoretical and empirical insight into when,
and how, we can expect data generated by a learned model to be useful. First,
we provide a simple theorem motivating how learning a model as an intermediate
step can narrow down the set of possible value functions more than learning a
value function directly from data using the Bellman equation. Second, we
provide an illustrative example showing empirically how a similar effect occurs
in a more concrete setting with neural network function approximation. Finally,
we provide extensive experiments showing the benefit of model-based learning
for online RL in environments with combinatorial complexity, but factored
structure that allows a learned model to generalize. In these experiments, we
take care to control for other factors in order to isolate, insofar as
possible, the benefit of using experience generated by a learned model relative
to ER alone.
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