Temporal Alignment for History Representation in Reinforcement Learning

TL;DR

TempAl is a self-supervised method that aligns temporally-close frames to create a compact history representation, improving reinforcement learning performance in partially observable environments.

Contribution

The paper introduces TempAl, a novel contrastive learning approach for automatic history representation in RL, inspired by human memory, enhancing environment understanding.

Findings

TempAl outperforms baseline in 35 of 49 Atari games.

It effectively captures slowly varying environment states.

The method is fully self-supervised and generalizable.

Abstract

Environments in Reinforcement Learning are usually only partially observable. To address this problem, a possible solution is to provide the agent with information about the past. However, providing complete observations of numerous steps can be excessive. Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision. Our method (TempAl) aligns temporally-close frames, revealing a general, slowly varying state of the environment. This procedure is based on contrastive loss, which pulls embeddings of nearby observations to each other while pushing away other samples from the batch. It can be interpreted as a metric that captures the temporal relations of observations. We propose to combine both common instantaneous and our history representation and we evaluate TempAl on all available Atari games from the Arcade Learning Environment. TempAl surpasses the instantaneous-only baseline in 35 environments out of 49. The source code of the method and of all the experiments is available at https://github.com/htdt/tempal.