Actor-Critic Reinforcement Learning for Continuous Action Control
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Solution Overview
Problem
Reinforcement learning systems struggle to effectively learn action selection policies in high-dimensional, continuous action spaces, particularly when the action space is intractable for discretization and when observations are either low-dimensional or high-dimensional pixel inputs.
Innovation Solution
A reinforcement learning system utilizing an actor neural network trained with a replay memory, critic neural network, and target neural networks to learn effective action selection policies, even in complex environments, by generating and updating experience tuples to refine parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems use traditional discretization methods for action spaces, then the action space becomes tractable for exploration, but the system cannot effectively learn in high-dimensional continuous action spaces that require fine control
Solution Approach 1:
The patent replaces traditional discretization-based exploration mechanisms with a neural network-based continuous action selection system. The actor neural network directly outputs continuous action values, eliminating the need for discrete action spaces and traditional exploration-exploitation trade-offs, thereby enabling effective learning in high-dimensional continuous action spaces.
Solution Approach 2:
The patent transforms the action space from discrete to continuous by changing the parameter representation. Instead of selecting from discrete action categories, the system uses continuous action values output by the neural network, allowing for fine-grained control and adaptation to high-dimensional continuous environments.
2Loss of information
If reinforcement learning systems use high-dimensional pixel inputs for observations, then the system can capture detailed environmental information, but the complexity of processing and learning from such inputs increases significantly
Solution Approach 1:
The patent introduces an encoder neural network as an intermediary between high-dimensional pixel inputs and the action selection process. This encoder processes and compresses the high-dimensional visual observations into meaningful feature representations, reducing computational complexity while preserving essential environmental information needed for effective action selection.
3Reliability
If reinforcement learning systems explore extensively in continuous action spaces, then they can discover effective policies, but the training time and computational resources required become intractable
Solution Approach 1:
The patent replaces traditional trial-and-error exploration with a neural network-based policy that directly maps observations to actions. The actor neural network learns to select actions based on observed states, eliminating the need for extensive random exploration and significantly reducing training time while maintaining policy effectiveness.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.