Generalised Counteractive Reinforcement Learning

Reinforcement learning has been the epicenter of immense scientific progress over the past decade, standing at the forefront of transformative advancements that enable agents to learn in high-dimensional spaces solely by interacting with an environment via trial and error without any supervision. Nonetheless, the learning process remains highly sensitive to the dimensions of the environment; as MDP complexity grows, the search space expands, agents continue to face a fundamental tension between complexity and policy success. Related to this disparity, recent work established a principled approach revealing that counteractive actions result in experiences with higher temporal differences. While influential, these results were founded on the assumption of discrete action spaces. In this work we focus on overcoming this tension and the agent’s interaction with high-dimensional MDPs. We introduce a theoretically grounded foundational paradigm for reinforcement learning that accelerates the learning process without introducing any computational complexity. Our analysis demonstrates that learning with counteractive actions is a principled basis for scalable and efficient training. We conduct extensive experiments in high-dimensional complex MDPs and the empirical results verify the theoretical analysis provided in our paper demonstrating that generalized counteractive reinforcement learning results in accelerated and effective learning.