# Reinforcement Learning from Hierarchical Critics

## TL;DR

This paper introduces RLHC, a hierarchical critic-based reinforcement learning algorithm that leverages global and local information to enhance learning speed and rewards in competitive multi-agent environments.

## Contribution

The paper proposes a novel RL algorithm using hierarchical critics to incorporate global information, improving training efficiency and performance in multi-agent competitions.

## Key findings

- RLHC outperforms PPO in tennis and soccer tasks
- Hierarchical critics improve coordination and learning speed
- Global information enhances reinforcement learning in competitive scenarios

## Abstract

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learning from hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against the benchmark algorithm, proximal policy optimisation (PPO), for two experimental scenarios performed in a Unity environment consisting of tennis and soccer agents' competitions. The results showed that RLHC outperforms the benchmark on both competition tasks.