2 research outputs found
Provably efficient reconstruction of policy networks
Recent research has shown that learning poli-cies parametrized by large
neural networks can achieve significant success on challenging reinforcement
learning problems. However, when memory is limited, it is not always possible
to store such models exactly for inference, and com-pressing the policy into a
compact representation might be necessary. We propose a general framework for
policy representation, which reduces this problem to finding a low-dimensional
embedding of a given density function in a separable inner product space. Our
framework allows us to de-rive strong theoretical guarantees, controlling the
error of the reconstructed policies. Such guaran-tees are typically lacking in
black-box models, but are very desirable in risk-sensitive tasks. Our
experimental results suggest that the reconstructed policies can use less than
10%of the number of parameters in the original networks, while incurring almost
no decrease in rewards
Deep Reinforcement and InfoMax Learning
We begin with the hypothesis that a model-free agent whose representations
are predictive of properties of future states (beyond expected rewards) will be
more capable of solving and adapting to new RL problems. To test that
hypothesis, we introduce an objective based on Deep InfoMax (DIM) which trains
the agent to predict the future by maximizing the mutual information between
its internal representation of successive timesteps. We test our approach in
several synthetic settings, where it successfully learns representations that
are predictive of the future. Finally, we augment C51, a strong RL baseline,
with our temporal DIM objective and demonstrate improved performance on a
continual learning task and on the recently introduced Procgen environment.Comment: NeurIPS 202