Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments (1710.03641v2)

Abstract: Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.

• The paper introduces a gradient-based meta-learning algorithm that significantly improves few-shot adaptation for reinforcement learning agents in dynamic settings.
• It employs a novel RoboSumo environment to rigorously evaluate continuous adaptation in multi-agent, competitive scenarios.
• Empirical results demonstrate enhanced sample efficiency and rapid policy adjustments, highlighting meta-learning's potential for real-world nonstationary challenges.

Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

The paper "Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments" investigates the problem of enabling reinforcement learning (RL) agents to adapt efficiently in dynamically changing environments. The authors focus on settings where the environment is nonstationary, either due to inherent complexity, changing dynamics, or the presence of multiple learning actors, such as in multi-agent systems.

Key Contributions

1. Gradient-based Meta-Learning Algorithm: The paper presents a gradient-based meta-learning algorithm designed for continuous adaptation. This approach leverages the learning-to-learn or "meta-learning" framework to enable rapid adaptation from limited experience, especially under few-shot learning constraints. It adapts agents' policies using strategies similar to model-agnostic meta-learning (MAML), reformulated for RL contexts.
2. RoboSumo Environment: To evaluate their algorithm, the authors design a novel multi-agent competitive environment called RoboSumo. This environment simulates sumo wrestling with robotic agents and introduces iterated adaptation games to test agents' continuous adaptation capabilities. These games require agents to learn and adapt their strategies during repeated interactions with opponents who are themselves learning and adapting.
3. Empirical Evaluation: Through experiments, the paper demonstrates that agents employing meta-learning can adapt more efficiently than reactive baseline methods in both single-agent and multi-agent scenarios. The results highlight significant improvements in sample efficiency and adaptation speed, particularly in few-shot learning regimes.
4. Population Dynamics and Evolution: The research further explores the evolution of a population of agents with diverse morphologies and adaptation strategies. By evaluating agents’ successes based on TrueSkill rankings in iterated games, the paper shows that those capable of rapid adaptation tend to dominate in multi-agent environments.

Implications and Future Research

This work provides substantial insights into continuous adaptation methodologies in RL, emphasizing meta-learning's potential to improve adaptability in nonstationary environments. The introduction of RoboSumo offers a challenging testbed for future research in competitive multi-agent RL scenarios. The algorithm's proficiency in few-shot adaptation sets a foundation for exploring more complex and real-world nonstationary environments, such as autonomous driving or dynamic resource allocation systems.

Future research can explore more scalable meta-learning methods to tackle large distributional shifts, where the current algorithm shows limitations. Furthermore, integrating auxiliary rewards or leveraging unsupervised learning could enhance adaptation in scenarios with sparse reward signals. The continuous adaptation framework proposed in this paper moves us closer to developing robust agents capable of online learning in ever-changing environments, a crucial step towards achieving general AI.

Overall, this paper makes a substantial contribution to the domain of adaptive learning in RL. By formalizing the continuous adaptation problem within a meta-learning framework, it opens new avenues for improving the resilience and efficiency of RL agents when faced with nonstationary and competitive challenges.