Preeti
Deep Q-learning faces challenges in learning with high dimensional domains, sparse rewards, and long horizons. Deep abstract Q-learning, an advanced reinforcement learning technique, can enhance performance in such scenarios. The Deep Abstract Q-network is introduced in this article, focusing on its definition, working, and applications.
The Deep Abstract Q-network is an advancement of traditional deep Q-learning, enabling reinforcement learning agents to train in high-dimensional domains with sparse rewards. It addresses problems where agents need to navigate complex environments to achieve long-term goals. Two approaches, novelty-based and abstraction-based, are used to tackle long-horizon planning challenges.
In the Deep Abstract Q-network, lightweight abstraction policies manage long-horizon domains and sparse rewards by dividing the domain into symbolic and pixel-based representations. The framework involves two agents, one operating on ground-level states and the other learning from abstractions provided by the experimenter.
The model processing involves abstracted states and actions, allowing the agent to plan at a higher level, and interactions between the two agents to define rewards and terminal functions. Applications of Deep Abstract Q-network include MAXQ, Option-Critic Architecture, Hierarchical-DQN, and Deep Abstract Q-Networks, all utilizing the abstraction-based approach to address high-dimensional domain and sparse reward challenges.
In conclusion, the Deep Abstract Q-network offers a framework for agents to learn and plan efficiently in long-horizon domains. By leveraging abstraction and hierarchical structures, this approach enhances the performance of reinforcement learning agents in complex environments.
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