Adaptive Meta-Learning via Simulated Environment Dynamics

Abstract

Meta-learning, the learning to learn, has shown significant promise in tackling complex tasks. However, a prevalent limitation lies in the reliance on static reward functions and environment dynamics, often simplifying the learning process and potentially hindering generalization to real-world scenarios. This paper introduces a novel adaptive meta-learning framework that addresses this limitation by dynamically adjusting the simulated environment's dynamics during the meta-training phase. The core idea is to utilize a learned Markov model to govern the environment's behavior, and to adapt the model parameters based on the agent's performance. This creates a continually evolving training environment, mirroring the inherent dynamism and uncertainty of real-world systems. We demonstrate that this approach leads to improved meta-learning performance compared to traditional static environment meta-learning methods. The algorithm incorporates key elements of reinforcement learning and Bayesian modeling to achieve adaptability and robustness. The primary formula representing the updated Markov model is: (Qt+1 | Qt, At) = f(Qt, At), where Qt+1 represents the state of the Markov model at time t+1, Qt is the state at time t, and At is the agent's action at time t. The function f is a parameterized function that is updated during the meta-training process. We explore the theoretical implications of this dynamic adaptation and discuss potential avenues for future research.