The Cell Must Go On: Agar.io for Continual Reinforcement Learning

TL;DR

This paper introduces AgarCL, a novel continual reinforcement learning platform based on the game Agar.io, to evaluate agents in a complex, evolving environment with stochastic dynamics and partial observability.

Contribution

The paper presents AgarCL, a new high-dimensional, non-episodic environment for continual RL research, along with benchmark results and analysis of existing continual learning methods.

Findings

Standard RL algorithms perform comparably to specialized continual learning methods in AgarCL.

AgarCL's environment complexity challenges current continual RL approaches.

Benchmark results for DQN, PPO, and SAC on AgarCL and its sub-tasks.

Abstract

Continual reinforcement learning (RL) concerns agents that are expected to learn continually, rather than converge to a policy that is then fixed for evaluation. This setting is well-suited to environments that the agent perceives as changing over time, rendering any static policy ineffective. In continual RL, researchers often simulate such changes either by modifying episodic environments to incorporate task shifts during interaction or by designing simulators that explicitly model continual dynamics. However, transforming episodic problems into continual ones primarily captures scenarios involving abrupt changes in the data stream and still relies on episodic structure. Meanwhile, the few simulators explicitly designed for empirical continual RL research are often limited in scope or complexity. In this paper, we introduce AgarCL, a research platform for continual RL that enables agents to progress toward increasingly sophisticated behaviour. AgarCL is based on the game Agar.io, a non-episodic, high-dimensional problem with stochastic, ever-evolving dynamics, continuous actions, and partial observability. We provide benchmark results for DQN, PPO, and SAC on the primary continual RL challenge, as well as across a suite of smaller tasks within AgarCL. These smaller tasks isolate aspects of the full environment and allow us to characterize the distinct challenges posed by different components of the game. We further evaluate three continual learning methods-Shrink and Perturb, ReDo, and Continual Backpropagation-and observe little improvement over standard RL algorithms, suggesting that the challenges posed by AgarCL extend beyond the stability-plasticity dilemma.