Rapid Open-World Adaptation by Adaptation Principles Learning
(2312.11138)Abstract
Novelty adaptation is the ability of an intelligent agent to adjust its behavior in response to changes in its environment. This is an important characteristic of intelligent agents, as it allows them to continue to function effectively in novel or unexpected situations, but still stands as a critical challenge for deep reinforcement learning (DRL). To tackle this challenge, we propose a simple yet effective novel method, NAPPING (Novelty Adaptation Principles Learning), that allows trained DRL agents to respond to different classes of novelties in open worlds rapidly. With NAPPING, DRL agents can learn to adjust the trained policy only when necessary. They can quickly generalize to similar novel situations without affecting the part of the trained policy that still works. To demonstrate the efficiency and efficacy of NAPPING, we evaluate our method on four action domains that are different in reward structures and the type of task. The domains are CartPole and MountainCar (classic control), CrossRoad (path-finding), and AngryBirds (physical reasoning). We compare NAPPING with standard online and fine-tuning DRL methods in CartPole, MountainCar and CrossRoad, and state-of-the-art methods in the more complicated AngryBirds domain. Our evaluation results demonstrate that with our proposed method, DRL agents can rapidly and effectively adjust to a wide range of novel situations across all tested domains.
Overview
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Introduces NAPPING (Novelty Adaptation Principles Learning), a method improving the adaptability of Deep Reinforcement Learning (DRL) agents to unencountered environments.
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NAPPING enables DRL agents to identify specific areas within a task that require adjustment and formulates tailored rules for adaptation without affecting other areas.
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Utilizes an embedded representation of states to learn new adaptation principles for enhancing policy performance in novel scenarios.
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Demonstrated superior performance over standard models across diverse domains such as CartPole, MountainCar, CrossRoad, and Angry Birds.
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Identifies limitations and outlines future research directions, including automatic detection of adaptation needs and extending to continuous action spaces.
Introduction to NAPPING
Deep Reinforcement Learning (DRL) has made significant strides in recent years, achieving remarkable success across various domains. However, one substantial limitation of DRL agents is their difficulty adapting to new environments, a scenario we often refer to as novelty adaptation. This challenge arises when an agent encounters situations or tasks within an environment that differ from what it was trained on; it frequently struggles to adjust accordingly. This paper introduces Novelty Adaptation Principles Learning (NAPPING), a novel method designed to enhance the adaptability of trained DRL agents to novel situations in such open-world environments.
Adapting to Change
Humans are adept at adjusting actions to account for new circumstances—a critical survival trait that current DRL agents do not possess intrinsically. Traditional approaches to teach agents adaptability, such as online learning and fine-tuning, have drawbacks, including the tendency to forget previously learned information when exposed to new data. NAPPING, by contrast, is a targeted approach. In a nutshell, it enables a DRL agent to identify specific areas within a task where its existing policy fails due to the changes. Subsequently, NAPPING devises new, tailored rules, called adaptation principles, only for these areas without harming established behaviors that do not require adjustment.
The Mechanics of NAPPING
At the core of NAPPING is the identification and delineation of states, or regions within a state space, where the agent's prior policy is insufficient or fails. It then learns new adaptation principles for these regions by utilizing an embedded representation of states derived from the trained agent, enhancing generalization to similar future states. NAPPING’s efficacy is rooted in learning and applying these principles only when and where necessary, as opposed to implementing a blanket adjustment across the entire policy which could degrade performance in unchanged parts of the environment.
Experimental Results
NAPPING's effectiveness was rigorously tested across four distinctive domains, each presenting various types of challenges: the control-based environments of CartPole and MountainCar, a path-finding task in CrossRoad, and the physical reasoning game Angry Birds. Compared to baseline models that employ online learning, fine-tuning, and even to recent open-world learning techniques, NAPPING displayed superior adaptability, efficiently responding to a multitude of novel scenarios. Not only did it outperform standard machine learning methods, but it also exhibited this increased adaptability with rapidity and effectiveness, suggesting that it is a prominent step forward in the field of reinforcement learning.
Looking Forward
Among the limitations of current NAPPING implementation is the reliance on a predefined performance evaluation function and threshold to identify when adaptations are needed. Addressing this would call for more nuanced methods for determining the necessity of adaptation without manual definition, which is a proposed vector for future research. Furthermore, extending NAPPING to be compatible with continuous action spaces and improving the method for selecting viable actions during adaptation are areas aimed to be developed, leading to a more broadly applicable and efficient methodology.
In conclusion, NAPPING represents a significant advancement in the advancement of DRL agents, equipping them with the much-needed ability to adapt to the ever-changing complexities of real-world environments. This innovation may very well be a defining moment in our journey to create truly intelligent and adaptable artificial agents.
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