Information asymmetry in KL-regularized RL

by   Alexandre Galashov, et al.

Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning. We start from the KL regularized expected reward objective which introduces an additional component, a default policy. Instead of relying on a fixed default policy, we learn it from data. But crucially, we restrict the amount of information the default policy receives, forcing it to learn reusable behaviors that help the policy learn faster. We formalize this strategy and discuss connections to information bottleneck approaches and to the variational EM algorithm. We present empirical results in both discrete and continuous action domains and demonstrate that, for certain tasks, learning a default policy alongside the policy can significantly speed up and improve learning.


page 6

page 17


Exploiting Hierarchy for Learning and Transfer in KL-regularized RL

As reinforcement learning agents are tasked with solving more challengin...

Towards an Understanding of Default Policies in Multitask Policy Optimization

Much of the recent success of deep reinforcement learning has been drive...

Local Search for Policy Iteration in Continuous Control

We present an algorithm for local, regularized, policy improvement in re...

SLACK: Stable Learning of Augmentations with Cold-start and KL regularization

Data augmentation is known to improve the generalization capabilities of...

PLOTS: Procedure Learning from Observations using Subtask Structure

In many cases an intelligent agent may want to learn how to mimic a sing...

Action-Sufficient State Representation Learning for Control with Structural Constraints

Perceived signals in real-world scenarios are usually high-dimensional a...

Learning Action Embeddings for Off-Policy Evaluation

Off-policy evaluation (OPE) methods allow us to compute the expected rew...

Please sign up or login with your details

Forgot password? Click here to reset