Population Action Selection Neural Network for Diverse Policy Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional reinforcement learning techniques often learn only a single action selection policy, which is inadequate for complex tasks, and may result in a homogeneous population of action selection policies that encode similar strategies, failing to leverage diverse and synergistic strategies required for tasks like multi-player games.
Innovation Solution
A system that uses a population action selection neural network to jointly represent multiple action selection policies, conditioning on strategy embeddings to implement diverse action selection policies, and employs self-play and distillation techniques to encode diverse strategies and prevent catastrophic forgetting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional reinforcement learning techniques are used to learn a single action selection policy, then the model complexity is reduced, but the ability to handle complex tasks and encode diverse strategies deteriorates
Solution Approach 1:
The patent segments the action selection process by maintaining a population of multiple distinct action selection policies instead of using a single policy. Each policy in the population can encode different strategies, allowing the system to handle complex tasks that require diverse behavioral approaches while keeping individual policy models relatively simple.
Solution Approach 2:
The patent combines multiple action selection policies into a unified population structure that works together through self-play and distillation mechanisms. This merging allows diverse strategies to be leveraged simultaneously, improving adaptability for complex tasks while managing overall system complexity through structured integration.
2Adaptability or versatility
If a population of action selection policies is used to encode diverse strategies, then the adaptability for complex tasks is improved, but the risk of catastrophic forgetting increases
Solution Approach 1:
The patent implements feedback mechanisms through self-play where policies in the population interact with each other, and through distillation where successful strategies are extracted and preserved. This feedback loop allows the system to continuously refine and maintain diverse strategies while preventing catastrophic forgetting by reinforcing effective behaviors across the population.
Solution Approach 2:
The patent employs distillation techniques that selectively recover and preserve successful strategies from the policy population. By identifying and retaining high-performing strategies while discarding or refining less effective ones, the system maintains adaptability for diverse tasks while preventing catastrophic forgetting of valuable learned behaviors.
3Productivity
If self-play and distillation techniques are employed to learn complementary strategies, then the performance of action selection policies is improved, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training individual policies in isolation before combining them into the population. This preliminary training phase allows each policy to develop basic competencies efficiently, reducing the overall training time required when the full population engages in self-play and distillation to achieve complementary strategies.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment using a population of action selection policies that are jointly represented by a population action selection neural network. In one aspect, a method comprises, at each of a plurality of time steps: obtaining an observation characterizing a current state of the environment at the time step; selecting a target action selection policy from the population of action selection policies; processing a network input comprising: (i) the observation, and (ii) a strategy embedding representing the target action selection policy, using the population action selection neural network to generate an action selection output; and selecting an action to be performed by the agent at the time step using the action selection output.