Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design (2012.02096v2)

Abstract: A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of tasks or environments in which a policy will be trained. However, creating a useful distribution of environments is error prone, and takes a significant amount of developer time and effort. We propose Unsupervised Environment Design (UED) as an alternative paradigm, where developers provide environments with unknown parameters, and these parameters are used to automatically produce a distribution over valid, solvable environments. Existing approaches to automatically generating environments suffer from common failure modes: domain randomization cannot generate structure or adapt the difficulty of the environment to the agent's learning progress, and minimax adversarial training leads to worst-case environments that are often unsolvable. To generate structured, solvable environments for our protagonist agent, we introduce a second, antagonist agent that is allied with the environment-generating adversary. The adversary is motivated to generate environments which maximize regret, defined as the difference between the protagonist and antagonist agent's return. We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED). Our experiments demonstrate that PAIRED produces a natural curriculum of increasingly complex environments, and PAIRED agents achieve higher zero-shot transfer performance when tested in highly novel environments.

• The paper introduces the PAIRED framework, which leverages unsupervised environment design to automatically generate curricula of complex tasks using a regret-based metric.
• It employs a dual-agent setup with a protagonist and an antagonist to foster emergent behaviors and improve zero-shot transfer in reinforcement learning.
• Experimental results demonstrate that PAIRED outperforms traditional methods in navigation tasks, ensuring robust generalization in novel, challenging environments.

Overview of Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

The paper introduces a novel approach termed Unsupervised Environment Design (UED) aimed at enhancing reinforcement learning (RL) methodologies to address challenges in robustness, transfer learning, unsupervised RL, and emergent complexity. One of the perennial problems in RL is the design of a suitable distribution of training environments. Constructing such an environment distribution is often labor-intensive and error-prone if done manually. The proposed UED framework automates the creation of task environments by discovering a distribution over a set of feasibly solvable environments. This paper critiques extant techniques such as domain randomization and minimax adversarial training, noting their respective weaknesses: inability to generate structure and often improper adaptation of environment difficulty.

Protagonist Antagonist Induced Regret Environment Design (PAIRED)

To overcome the issues identified with current UED approaches, the authors present the PAIRED technique. PAIRED leverages a two-agent framework consisting of a protagonist (the primary learning agent) and an antagonist (an auxiliary agent learning under the same conditions), with a shared adversary. The goal of the adversary is to design environments that maximize the regret, defined by the performance differential between the protagonist and antagonist. The regret-based metric naturally fosters a structured curriculum of increasingly complex environments, allowing the protagonist to achieve improved zero-shot transfer performance in novel settings compared to baseline approaches.

Experimental Results

The empirical evaluation is twofold. Firstly, the ability of PAIRED to engender emergent complex behaviors in agents was scrutinized through navigation tasks in partially observable environments. Compared to seminal techniques—domain randomization and minimax adversarial environment generation—PAIRED showed a notable increment in task complexity that agents could solve. This substantiates PAIRED's efficacy in auto-curricula formation. Secondly, in zero-shot transfer tests involving a shift to novel environments not encountered during the training phase, PAIRED exhibited superior robustness and adaptability. PAIRED significantly outperformed baselines in environments with intricate structures designed to be highly challenging for navigation, thus validating the hypothesis that regret-informed adversarial environment generation aids generalization across unanticipated real-world scenarios.

Implications

The PAIRED framework presents several implications for the field of AI, specifically in the context of RL-based systems requiring robust adaptation to volatility in deployment environments. The emphasis on constructing environments that facilitate learning across a spectrum of unforeseen conditions is crucial for RL application in dynamic real-world settings, such as autonomous navigation or robotic manipulation. Moreover, the flexible environment design posited by PAIRED can reduce developer overhead associated with the traditionally manual task of environment specification.

From a theoretical standpoint, the formalization of UED and its relationship to decision-making under uncertainty, mapped through classical decision theory concepts like minimax regret, provides a scaffold for further research. It opens avenues for exploration into other decision protocols that might be adapted as environment policies under the UED framework.

Conclusion

In summary, the paper presents the PAIRED algorithm, an innovative approach to RL environment design that addresses critical limitations in emergent complexity and zero-shot transfer. By utilizing a regret-based curriculum, PAIRED matches or exceeds existing methods in equipping RL agents with the ability to generalize across diverse and complex scenarios. This contribution adds to the growing body of knowledge in autonomous systems where adaptability and robustness are of paramount importance. As RL techniques continue to evolve, methodologies like PAIRED play a pivotal role in enhancing the reliability and performance of AI systems in the complex interplay of the real world.