Graph Vertex Embeddings: Distance, Regularization and Community Detection

Graph embeddings have emerged as a powerful tool for representing complex network structures in a low-dimensional space, enabling the use of efficient methods that employ the metric structure in the embedding space as a proxy for the topological structure of the data. In this paper, we explore several aspects that affect the quality of a vertex embedding of graph-structured data. To this effect, we first present a family of flexible distance functions that faithfully capture the topological distance between different vertices. Secondly, we analyze vertex embeddings as resulting from a fitted transformation of the distance matrix rather than as a direct result of optimization. Finally, we evaluate the effectiveness of our proposed embedding constructions by performing community detection on a host of benchmark datasets. The reported results are competitive with classical algorithms that operate on the entire graph while benefitting from a substantially reduced computational complexity due to the reduced dimensionality of the representations.

Actor-Critic with variable time discretization via sustained actions

Reinforcement learning (RL) methods work in discrete time. In order to apply RL to inherently continuous problems like robotic control, a specific time discretization needs to be defined. This is a choice between sparse time control, which may be easier to train, and finer time control, which may allow for better ultimate performance. In this work, we propose SusACER, an off-policy RL algorithm that combines the advantages of different time discretization settings. Initially, it operates with sparse time discretization and gradually switches to a fine one. We analyze the effects of the changing time discretization in robotic control environments: Ant, HalfCheetah, Hopper, and Walker2D. In all cases our proposed algorithm outperforms state of the art.

Reinforcement learning for optimization of energy trading strategy

An increasing part of energy is produced from renewable sources by a large number of small producers. The efficiency of these sources is volatile and, to some extent, random, exacerbating the energy market balance problem. In many countries, that balancing is performed on day-ahead (DA) energy markets. In this paper, we consider automated trading on a DA energy market by a medium size prosumer. We model this activity as a Markov Decision Process and formalize a framework in which a ready-to-use strategy can be optimized with real-life data. We synthesize parametric trading strategies and optimize them with an evolutionary algorithm. We also use state-of-the-art reinforcement learning algorithms to optimize a black-box trading strategy fed with available information from the environment that can impact future prices.

Emergency action termination for immediate reaction in hierarchical reinforcement learning

Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether they are still worth pursuing. However, due to the randomness of the environment, these goals may become obsolete. In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level. If the actions, i.e. lower level goals, become inadequate, they are replaced by more appropriate ones. This way we combine the advantages of hierarchical RL, which is fast training, and flat RL, which is immediate reactivity. We study our approach experimentally on seven benchmark environments.

Reinforcement learning with experience replay and adaptation of action dispersion

Effective reinforcement learning requires a proper balance of exploration and exploitation defined by the dispersion of action distribution. However, this balance depends on the task, the current stage of the learning process, and the current environment state. Existing methods that designate the action distribution dispersion require problem-dependent hyperparameters. In this paper, we propose to automatically designate the action distribution dispersion using the following principle: This distribution should have sufficient dispersion to enable the evaluation of future policies. To that end, the dispersion should be tuned to assure a sufficiently high probability (densities) of the actions in the replay buffer and the modes of the distributions that generated them, yet this dispersion should not be higher. This way, a policy can be effectively evaluated based on the actions in the buffer, but exploratory randomness in actions decreases when this policy converges. The above principle is verified here on challenging benchmarks Ant, HalfCheetah, Hopper, and Walker2D, with good results. Our method makes the action standard deviations converge to values similar to those resulting from trial-and-error optimization.

Graph autoencoder with constant dimensional latent space

Invertible transformation of large graphs into constant dimensional vectors (embeddings) remains a challenge. In this paper we address it with recursive neural networks: The encoder and the decoder. The encoder network transforms embeddings of subgraphs into embeddings of larger subgraphs, and eventually into the embedding of the input graph. The decoder does the opposite. The dimension of the embeddings is constant regardless of the size of the (sub)graphs. Simulation experiments presented in this paper confirm that our proposed graph autoencoder can handle graphs with even thousands of vertices.

Logarithmic Continual Learning

We introduce a neural network architecture that logarithmically reduces the number of self-rehearsal steps in the generative rehearsal of continually learned models. In continual learning (CL), training samples come in subsequent tasks, and the trained model can access only a single task at a time. To replay previous samples, contemporary CL methods bootstrap generative models and train them recursively with a combination of current and regenerated past data. This recurrence leads to superfluous computations as the same past samples are regenerated after each task, and the reconstruction quality successively degrades. In this work, we address these limitations and propose a new generative rehearsal architecture that requires at most logarithmic number of retraining for each sample. Our approach leverages allocation of past data in a~set of generative models such that most of them do not require retraining after a~task. The experimental evaluation of our logarithmic continual learning approach shows the superiority of our method with respect to the state-of-the-art generative rehearsal methods.

Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning

We propose a new method for unsupervised continual knowledge consolidation in generative models that relies on the partitioning of Variational Autoencoder's latent space. Acquiring knowledge about new data samples without forgetting previous ones is a critical problem of continual learning. Currently proposed methods achieve this goal by extending the existing model while constraining its behavior not to degrade on the past data, which does not exploit the full potential of relations within the entire training dataset. In this work, we identify this limitation and posit the goal of continual learning as a knowledge accumulation task. We solve it by continuously re-aligning latent space partitions that we call bands which are representations of samples seen in different tasks, driven by the similarity of the information they contain. In addition, we introduce a simple yet effective method for controlled forgetting of past data that improves the quality of reconstructions encoded in latent bands and a latent space disentanglement technique that improves knowledge consolidation. On top of the standard continual learning evaluation benchmarks, we evaluate our method on a new knowledge consolidation scenario and show that the proposed approach outperforms state-of-the-art by up to twofold across all testing scenarios.

Deep Memory Update

Recurrent neural networks are key tools for sequential data processing. Existing architectures support only a limited class of operations that these networks can apply to their memory state. In this paper, we address this issue and introduce a recurrent neural module called Deep Memory Update (DMU). This module is an alternative to well-established LSTM and GRU. However, it uses a universal function approximator to process its lagged memory state. In addition, the module normalizes the lagged memory to avoid gradient exploding or vanishing in backpropagation through time. The subnetwork that transforms the memory state of DMU can be arbitrary. Experimental results presented here confirm that the previously mentioned properties of the network allow it to compete with and often outperform state-of-the-art architectures such as LSTM and GRU.