Model-Free Reinforcement Learning Controller for Action Selection
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Solution Overview
Problem
Reinforcement learning systems face challenges in selecting optimal actions in dynamic environments, particularly when dealing with high-dimensional input streams and real-world applications, where existing methods struggle to efficiently learn control policies and extract relevant information.
Innovation Solution
A reinforcement learning system that employs a model-free controller using return data to map observation-action pairs to respective returns, allowing it to select actions based on historical performance, and updates this data dynamically, incorporating techniques like neural networks and variational auto-encoders for feature representation and memory optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems use neural networks to select actions in high-dimensional input streams, then the ability to process complex environmental data is improved, but the computational complexity and time required to learn effective control policies increases significantly
Solution Approach 1:
The patent segments the action selection process into multiple components: a neural network that processes high-dimensional observations and outputs action probabilities, and a separate sampling mechanism that selects actions based on these probabilities. This segmentation allows the system to handle complex data without requiring the neural network to directly output final actions, reducing computational burden.
Solution Approach 2:
The system performs preliminary processing of environmental observations through the neural network to generate action probability distributions before final action selection. This preliminary action prepares the data in a more manageable form, reducing the complexity of the subsequent action selection step while maintaining the ability to handle high-dimensional inputs.
2Measurement precision
If reinforcement learning systems process high-dimensional input streams in real-world applications, then the quality of environmental understanding is improved, but the time required to learn and adapt to new environments increases
Solution Approach 1:
The patent uses a neural network to create a probabilistic representation (copy) of the action selection policy based on processed observations. Instead of directly learning from raw high-dimensional inputs, the system learns from the neural network's probability distributions, which are lower-dimensional summaries that capture essential environmental information while accelerating learning.
Solution Approach 2:
The system replaces direct trial-and-error learning from raw high-dimensional inputs with a neural network-based probabilistic model. This substitution transforms the mechanical process of learning from complex inputs into a more efficient probabilistic inference process, reducing the time required to adapt to new environments while maintaining understanding quality.
3Productivity
If reinforcement learning agents use model-based control, then the ability to plan and predict future states is improved, but the computational resources and complexity required to maintain environment models increases
Solution Approach 1:
The patent extracts the essential planning and prediction capabilities from complex environment models and implements them through a simpler model-free approach. The neural network learns to directly map observations to action probabilities without maintaining explicit environment models, extracting only the necessary predictive information needed for effective action selection while eliminating the burden of model maintenance.
Solution Approach 2:
The system changes the fundamental parameter of the control approach from model-based (requiring environment dynamics models) to model-free (using direct observation-to-action mappings). This parameter change maintains planning and prediction abilities through learned probability distributions while dramatically reducing the complexity associated with maintaining and updating environment models.
Data Source
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AI summary
Methods, systems, and apparatus for selecting actions to be performed by an agent interacting with an environment. One method includes maintaining return data that maps each observation-action pair to a respective return, the action in each observation-action pair being an action that was performed by the agent in response to the observation in the observation-action pair and the respective return mapped to by each of the observation-action pairs being a return that resulted from the agent performing the action in the observation-action pair; receiving a current observation; determining whether the current observation matches any observation identified in the return data; and in response to determining that the current observation matches a first observation identified in the return data, selecting an action to be performed by the agent using the returns mapped to by observation-action pairs in the return data that include the first observation.