GAN Pre-training for Reinforcement Learning Data Scarcity
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Solution Overview
Problem
Self-learning systems using deep reinforcement learning face challenges in achieving initial online performance in real environments due to the need for extensive training iterations and limited data collection, leading to poor initial performance and uncertainty in decision-making.
Innovation Solution
A pre-training system based on a modified Generative Adversarial Network (GAN) that uses state-action pair relations to generate vast amounts of realistic data, enhancing the accuracy of reinforcement learning by training a generator and discriminator within a virtualized environment, and updating policies using synthetic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the agent is pre-trained with demonstration data from the real environment, then the agent can learn faster, but it is unfeasible to collect a large amount of demonstration data and adds extra uncertainty margins for unseen state-actions
Solution Approach 1:
The patent uses a Generative Adversarial Network (GAN) to create synthetic copies of real environment data. The generator creates artificial state-action-reward tuples that statistically resemble real data, allowing the agent to be pre-trained on these synthetic copies without needing to collect extensive real demonstration data. This resolves the contradiction by providing unlimited synthetic data copies that maintain the essential statistical properties of real environment data.
Solution Approach 2:
The patent introduces a GAN as an intermediary system between the real environment and the agent training process. The GAN includes a generator that creates synthetic data and a discriminator that validates it against real data distributions. This intermediary enables the agent to train on synthetic data that captures the essential characteristics of the real environment without requiring direct access to large quantities of real demonstration data, thus resolving the data collection feasibility issue.
2Measurement precision
If the agent learns in the real domain with real consequences, then the learning is more accurate, but millions of poor decisions during learning are not acceptable in real life situations
Solution Approach 1:
The patent applies preliminary action by pre-training the agent on synthetic data generated by the GAN before deploying it to the real environment. The synthetic data is generated in advance and contains carefully constructed state-action-reward relationships that reflect the real environment's dynamics. This preliminary training allows the agent to learn accurate policies without making harmful decisions in the real environment during the learning process, as the synthetic data simulates the consequences of actions safely.
Solution Approach 2:
The GAN creates synthetic copies of real environment data that preserve the essential statistical properties and causal relationships. These synthetic copies allow the agent to practice and learn from millions of virtual trials without causing real-world harm. The synthetic data is indistinguishable from real data in terms of statistical properties, ensuring learning accuracy while eliminating the harmful effects of real-world trial and error.
3Device complexity
If a standard GAN is used for data generation, then the data generation process is simple, but it cannot accurately capture the characteristics of the environment and generates insufficient realistic data
Solution Approach 1:
The patent enhances the standard GAN by introducing a relations network as an intermediary component that specifically processes state-action pairs. This relations network is trained to extract and preserve the causal relationships between states, actions, and rewards. The generator uses this enhanced processing to create synthetic data that accurately captures the environmental characteristics and causal dynamics, going beyond what a standard GAN can achieve while maintaining relative simplicity through modular architecture.
Solution Approach 2:
The patent applies local quality by focusing the enhanced processing on specific aspects of the data - namely the state-action pairs and their causal relationships. The relations network specifically targets and enhances the representation of these critical relationships in the synthetic data generation process. This localized enhancement ensures that the most important environmental characteristics (causal relationships) are accurately captured without requiring complete redesign of the entire data generation system.
Data Source
AI summary
A pre-training apparatus and method for reinforcement learning based on a Generative Adversarial Network (GAN) is provided. GAN includes a generator and a discriminator. The method comprising receiving training data from a real environment where the training data includes a data slice corresponding to a first state-reward pair and a first state-action pair, training the GAN using the training data, training a relations network to extract a latent relationship of the first state-action pair with the first state-reward pair in a reinforcement learning context, causing the generator trained with training data to generate first synthetic data, processing a portion of the first synthetic data in the relations network to generate a resulting data slice, merging the second state-action pair portion of the first synthetic data with the second state-reward pair from the relations network to generate second synthetic data to update a policy for interaction with the real environment.