# Preliminary Tests of the Anticipatory Classifier System with Hindsight Experience Replay

arXiv: https://arxiv.org/abs/2601.09400 · HTML: https://mlsanity.com/paper/2601.09400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [XCS Classifier System with Experience Replay](https://mlsanity.com/paper/2002.05628/) (2020)

## Abstract (arXiv)

This paper introduces ACS2HER, a novel integration of the Anticipatory Classifier System (ACS2) with the Hindsight Experience Replay (HER) mechanism. While ACS2 is highly effective at building cognitive maps through latent learning, its performance often stagnates in environments characterized by sparse rewards. We propose a specific architectural variant that triggers hindsight learning when the agent fails to reach its primary goal, re-labeling visited states as virtual goals to densify the learning signal. The proposed model was evaluated on two benchmarks: the deterministic textttMaze 6 and the stochastic textttFrozenLake. The results demonstrate that ACS2HER significantly accelerates knowledge acquisition and environmental mastery compared to the standard ACS2. However, this efficiency gain is accompanied by increased computational overhead and a substantial expansion in classifier numerosity. This work provides the first analysis of combining anticipatory mechanisms with retrospective goal-relabeling in Learning Classifier Systems.