Bootstrapped Deep Reinforcement Learning for Efficient Exploration
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Solution Overview
Problem
Existing reinforcement learning approaches in deep learning networks face challenges in complex environments due to inefficient exploration strategies, particularly in large-scale applications, where systems struggle to balance exploration and exploitation, leading to suboptimal performance in environments with unknown dynamics and delayed consequences.
Innovation Solution
The implementation of a bootstrap technique that combines observed and artificially generated data to approximate a posterior distribution, allowing for efficient exploration and exploitation in deep learning networks, using a combination of observed and artificially generated data to induce a prior distribution, which is critical for effective exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional reinforcement learning approaches are used in deep learning networks, then the system can learn from observed data, but exploration efficiency deteriorates in complex environments with large state spaces
Solution Approach 1:
The patent uses bootstrapped copies of the neural network to generate artificial data that mimics observed data distributions. These synthetic trajectories are created by sampling from the bootstrapped networks' predictions, effectively copying and amplifying useful patterns from limited observed data to enhance exploration efficiency in complex environments.
Solution Approach 2:
The system performs preliminary exploration by generating artificial data trajectories before actual execution. The bootstrapped networks pre-compute potential actions and outcomes, allowing the system to explore virtual environments beforehand and learn from these pre-computed scenarios, thereby improving exploration efficiency without requiring extensive real-world trial and error.
2Productivity
If exploration strategies are simplified to handle large state spaces, then computational tractability improves, but learning performance deteriorates due to insufficient exploration
Solution Approach 1:
Instead of exhaustively exploring all possible states in the large state space, the patent generates artificial data by copying and combining patterns from observed data through bootstrapped networks. This creates a representative sample of useful states and transitions, improving learning performance while avoiding the time-consuming task of complete state space exploration.
Solution Approach 2:
The patent changes the parameter space by transforming raw observed data into bootstrapped network predictions with associated uncertainties. This transformation creates a compressed representation of the environment that captures essential patterns while reducing the effective state space size, enabling better exploration within limited training time.
3Adaptability or versatility
If the system relies solely on observed data for training, then data requirements are minimized, but generalization capability deteriorates in unknown environments
Solution Approach 1:
The bootstrapped networks generate artificial data trajectories by copying and combining patterns from observed data. These synthetic trajectories expand the effective data quantity without requiring additional real observations, enabling the system to generalize better to unknown environments while minimizing the need for extensive data collection.
Solution Approach 2:
The system performs preliminary data generation using bootstrapped networks to create artificial training data before actual deployment. This pre-computed artificial data enriches the training set with diverse scenarios and edge cases, improving generalization capability without requiring the system to wait for or rely solely on future observed data.
Data Source
AI summary
Systems and methods for providing reinforcement learning for a deep learning network are disclosed. A reinforcement learning process that provides deep exploration is provided by a bootstrap that applied to a sample of observed and artificial data to facilitate deep exploration via a Thompson sampling approach.