Web Navigation Agents Using Hierarchical RL for Sparse Rewards
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Solution Overview
Problem
Reinforcement learning in environments with large state and action spaces and sparse rewards is challenging, particularly for automated assistants navigating interactive web documents, where tasks like booking flights require complex interactions with numerous options and sparse feedback, making trial-and-error inefficient.
Innovation Solution
Implementing deep Q network agents with hierarchical reinforcement learning and curriculum learning to decompose complex instructions into simpler sub-instructions, using dense potential-based rewards, and generating synthetic training examples with a meta-trainer to enhance training efficiency, especially when expert demonstrations are unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is used to train a web navigation agent, then the agent can automatically navigate web documents, but the training becomes cumbersome due to large state and action spaces and sparse rewards
Solution Approach 1:
The patent segments the navigation task into a hierarchical structure with high-level task planning and low-level action execution. The navigation policy is divided into multiple levels where higher levels make strategic decisions about which sections to visit, and lower levels handle specific interactions with web elements. This segmentation reduces the effective state and action spaces at each level, making training more efficient.
Solution Approach 2:
The patent pre-computes and stores information about web document structures, element relationships, and navigation patterns before training begins. This preliminary processing creates structured representations that guide the reinforcement learning process, reducing the exploration space and enabling faster convergence during training.
2Reliability
If the agent explores all possible options in large state and action spaces, then it may find the correct navigation path, but the training becomes infeasible due to the sheer number of combinations
Solution Approach 1:
The patent applies local quality by creating specialized processing components for different types of web elements and navigation contexts. Instead of treating all states and actions uniformly, the system uses element-type-specific features and transition models that are tailored to particular web component categories, improving both accuracy and efficiency by focusing computational resources where they are most needed.
Solution Approach 2:
The patent transforms the navigation problem by changing the parameterization of states and actions. It uses learned representations that compress the vast state space into meaningful semantic categories, and parameterizes actions in terms of task-relevant operations rather than raw DOM manipulations. This parameter transformation makes the problem tractable while preserving navigation accuracy.
3Productivity
If dense rewards are used to augment training, then the agent learns more efficiently, but the reward design becomes more complex
Solution Approach 1:
The patent introduces intermediate reward signals that mediate between the final navigation goal and individual actions. These intermediate rewards are based on progress toward task objectives, such as successfully identifying relevant sections or completing sub-tasks. The intermediary reward structure provides guidance without requiring complex hand-crafted reward functions, balancing training efficiency with implementation simplicity.
Data Source
AI summary
The present disclosure is generally directed to methods, apparatus, and computer-readable media (transitory and non-transitory) for learning to automatically navigate interactive web documents and/or websites. More particularly, various approaches are presented for training various deep Q network (DQN) agents to perform various tasks associated with reinforcement learning, including hierarchical reinforcement learning, in challenging web navigation environments with sparse rewards and large state and action spaces. These agents include a web navigation agent that can use learned value function(s) to automatically navigate through interactive web documents, as well as a training agent, referred to herein as a “meta-trainer,” that can be trained to generate synthetic training examples. Some approaches described herein may be implemented when expert demonstrations are available. Other approaches described herein may be implemented when expert demonstrations are not available. In either case, dense, potential-based rewards may be used to augment the training.