Jacob E. Kooi

Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet

In the context of MDPs with high-dimensional states, downstream tasks are predominantly applied on a compressed, low-dimensional representation of the original input space. A variety of learning objectives have therefore been used to attain useful representations. However, these representations usually lack interpretability of the different features. We present a novel approach that is able to disentangle latent features into a controllable and an uncontrollable partition. We illustrate that the resulting partitioned representations are easily interpretable on three types of environments and show that, in a distribution of procedurally generated maze environments, it is feasible to interpretably employ a planning algorithm in the isolated controllable latent partition.

Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet

Activation functions are one of the key components of a deep neural network. The most commonly used activation functions can be classed into the category of continuously differentiable (e.g. tanh) and piece-wise linear functions (e.g. ReLU), both having their own strengths and drawbacks with respect to downstream performance and representation capacity through learning. In reinforcement learning, the performance of continuously differentiable activations often falls short as compared to piece-wise linear functions. We show that the dying neuron problem in RL is not exclusive to ReLUs and actually leads to additional problems in the case of continuously differentiable activations such as tanh. To alleviate the dying neuron problem with these activations, we propose a Hadamard representation that unlocks the advantages of continuously differentiable activations. Using DQN, PPO and PQN in the Atari domain, we show faster learning, a reduction in dead neurons and increased effective rank.