State-Dependent Action Space Quantization for RL
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Solution Overview
Problem
Machine learning models, particularly those using reinforcement learning, face challenges in controlling agents with large and complex action spaces due to the curse of dimensionality, making it difficult for policy neural networks to effectively search and identify optimal actions in such spaces.
Innovation Solution
The implementation of a discretization neural network that performs state-dependent action space quantization, mapping a large action space to a smaller one, allowing policy neural networks trained with discrete actions to control agents with continuous or complex action spaces by selecting actions from a reduced set, thereby improving training efficiency and exploration capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a policy neural network directly controls an agent with a large continuous action space, then the agent can perform complex tasks requiring fine-grained control, but the policy neural network cannot effectively search through the large space to identify optimal actions
Solution Approach 1:
The continuous action space is segmented into multiple discrete action bins or regions. The discretization neural network divides the large continuous action space into smaller manageable segments, allowing the policy network to select from discrete action indices rather than searching the entire continuous space. This segmentation reduces the search complexity while maintaining coverage of the full action space through the discretization mapping.
2Device complexity
If the action space is discretized using existing techniques, then the action space complexity is reduced, but the policy neural network performance deteriorates due to the curse of dimensionality
Solution Approach 1:
A discretization neural network is introduced as an intermediary component between the policy neural network and the agent. This intermediary network learns to map continuous action space to discrete action bins in a state-dependent manner, bridging the gap between continuous control requirements and discrete action selection. The learned discretization preserves important action distinctions while reducing complexity, avoiding the performance degradation associated with naive discretization methods.
3Loss of time
If a discrete action reinforcement learning technique is used, then the training is more sample efficient, but the method cannot be directly applied to continuous action spaces
Solution Approach 1:
The action space parameters are transformed from continuous to discrete through the discretization neural network. By learning an optimal discretization mapping that adapts to the state, the system enables discrete action reinforcement learning algorithms to operate in continuous action spaces. The parameter transformation is state-dependent and learned during training, allowing discrete RL methods to achieve sample efficiency while maintaining applicability to continuous control tasks.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using a discretization neural network that generates a state-dependent discretization of an original action space and a policy neural network that is used to select an action from the state-dependent quantization rather than from the original action space.