Generative Flow Network Training Through Continuous Action Sampling
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Solution Overview
Problem
Generative flow networks are limited to deterministic discrete environments and struggle to be applied to continuous control tasks due to infinite state and action spaces in real-world scenarios.
Innovation Solution
A method for training generative flow networks by sampling actions from a continuous action space, using a loss function to update the network, enabling it to process continuous control tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative flow networks are applied to deterministic discrete environments, then the network can be trained and processed effectively, but the network cannot handle continuous control tasks with infinite state and action spaces
Solution Approach 1:
The continuous action space is segmented into multiple discrete action buckets through quantization. Each bucket represents a discrete action category, transforming the infinite continuous space into a finite discrete structure that the generative flow network can process effectively.
Solution Approach 2:
The patent changes the parameter representation of actions from continuous values to discrete bucket indices. By applying quantization parameters and bucket assignments, continuous action parameters are transformed into discrete categories that maintain the essential characteristics of the continuous space while enabling efficient network processing.
2Reliability
If reinforcement learning is used to maximize expected future reward, then high reward action sequences are generated, but sampling is limited to high reward areas reducing exploration capability
Solution Approach 1:
The generative flow network dynamically adjusts sampling probabilities based on the learned flow structure rather than relying solely on reward magnitude. This dynamic sampling approach allows the system to explore diverse action sequences while still converging toward high-reward regions, balancing exploration and exploitation.
Solution Approach 2:
The patent introduces a flow-based intermediary structure that mediates between the reward signal and the sampling process. This flow network learns to distribute probability mass across different action sequences, enabling exploration of diverse candidates while being guided toward high-reward regions through the learned flow dynamics.
Data Source
AI summary
A method for training a generative flow network is provided, and is applied to the field of artificial intelligence technologies. In the method, in a process of training the generative flow network, for any state of an agent, a plurality of first actions performed in the state and a plurality of second actions that can be transferred to the state are selected from a continuous action space in a sampling manner, then, predicted values corresponding to the plurality of first actions and the plurality of second actions are output by using the generative flow network, and further, a loss function used to update the generative flow network is obtained through calculation. In this solution, a plurality of actions obtained through sampling are used to approximately represent the continuous action space, and then, the generative flow network is trained.