Entropy Regularized Reinforcement Learning with Cascading Networks

TL;DR

This paper introduces a growing neural network architecture for entropy-regularized reinforcement learning, improving policy stability and convergence on some benchmarks by controlling policy change rate.

Contribution

It proposes a novel neural network growth strategy for RL that enables closed-form entropy-regularized policy updates, addressing non-i.i.d. data challenges.

Findings

Achieves better convergence on certain RL benchmarks.

Demonstrates improved policy stability during training.

Shows limitations on some tasks compared to baselines.

Abstract

Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL uses neural networks as function approximators. These neural models are largely inspired by developments in the (un)supervised machine learning community. Compared to these learning frameworks, one of the major difficulties of RL is the absence of i.i.d. data. One way to cope with this difficulty is to control the rate of change of the policy at every iteration. In this work, we challenge the common practices of the (un)supervised learning community of using a fixed neural architecture, by having a neural model that grows in size at each policy update. This allows a closed form entropy regularized policy update, which leads to a better control of the rate of change of the policy at each iteration and help cope with the non i.i.d. nature of RL. Initial experiments on classical RL benchmarks show promising results with remarkable convergence on some RL tasks when compared to other deep RL baselines, while exhibiting limitations on others.