Title: Action-Adaptive Continual Learning: Enabling Policy Generalization under Dynamic Action Space
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: continual learning, lifelong learning, continual reinforcement learning

Abstract: Continual Learning (CL) is a powerful tool that enables agents to learn a sequence of tasks, accumulating knowledge learned in the past and using it for problem-solving or future task learning. However, existing CL methods often assume that the agent’s capabilities remain static within dynamic environments, which doesn’t reflect real-world scenarios where capabilities dynamically change. This paper introduces a new and realistic problem: *Continual Learning with Dynamic Capabilities* (CL-DC), posing a significant challenge for CL agents: How can policy generalization across different action spaces be achieved? Inspired by the cortical functions, we propose an **A**ction-**A**daptive **C**ontinual **L**earning framework (AACL) to address this challenge. Our framework decouples the agent’s policy from the specific action space by building an action representation space. For a new action space, the encoder-decoder of action representations is adaptively fine-tuned to maintain a balance between stability and plasticity. Furthermore, we release a benchmark based on three environments to validate the effectiveness of methods for CL-DC. Experimental results demonstrate that our framework outperforms popular methods by generalizing the policy across action spaces.