Reinforcement Learning Algorithm Search Across Diverse Environments
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Solution Overview
Problem
Designing new deep reinforcement learning algorithms that can efficiently solve a wide variety of problems generally requires a tremendous amount of manual effort, and automating the process of developing domain-independent reinforcement learning algorithms has been limited.
Innovation Solution
A system and method for generating and searching reinforcement learning algorithms using a computer system that performs training and evaluation on a diverse set of environments, utilizing a search language with neural network submodules and general symbolic loss functions to produce algorithms that can generalize to new environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design methods are used for reinforcement learning algorithms, then design flexibility and understanding are maintained, but the time and effort required increases tremendously
Solution Approach 1:
The system enables self-service by allowing the algorithm to automatically design reinforcement learning algorithms through neural network processing. The computer system receives training data, processes it through the neural network to generate algorithm components, and outputs complete reinforcement learning algorithms without requiring manual intervention at each design step, thereby dramatically improving productivity while reducing design time.
2Productivity
If automated methods are used to generate reinforcement learning algorithms, then design time is reduced, but the ability to generalize to new environments is limited
Solution Approach 1:
The system implements feedback by training the neural network on a diverse set of training environments and using the performance results to iteratively improve the algorithm generation process. The neural network learns from the feedback of which algorithm components work well in which environments, enabling it to generate algorithms that generalize better to new, unseen environments while maintaining high productivity.
Solution Approach 2:
The system achieves universality by designing the neural network to handle multiple types of reinforcement learning algorithm components (reward functions, transition models, policy gradients) and multiple environment types simultaneously. This multi-functional approach allows the generated algorithms to be applied across diverse domains and generalize to new environments, rather than being specialized for a single task.
3Adaptability or versatility
If diverse training environments are used, then algorithm generalization improves, but the complexity of the training process increases
Solution Approach 1:
The system applies segmentation by breaking down the complex training process into distinct modular components: data collection from multiple environments, separate neural network processing stages, individual algorithm component generation, and staged evaluation. This segmentation allows the system to manage complexity while processing diverse training environments, as each module can be independently optimized and the overall process remains organized and scalable.
Data Source
AI summary
Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating and searching reinforcement learning algorithms. In some implementations, a computer-implemented system generates a sequence of candidate reinforcement learning algorithms. Each candidate reinforcement learning algorithm in the sequence is configured to receive an input environment state characterizing a state of an environment and to generate an output that specifies an action to be performed by an agent interacting with the environment. For each candidate reinforcement learning algorithm in the sequence, the system performs a performance evaluation for a set of a plurality of training environments. For each training environment, the system adjusts a set of environment-specific parameters of the candidate reinforcement learning algorithm by performing training of the candidate reinforcement learning algorithm to control a corresponding agent in the training environment. The system generates an environment-specific performance metric for the candidate reinforcement learning algorithm that measures a performance of the candidate reinforcement learning algorithm in controlling the corresponding agent in the training environment as a result of the training. After performing training in the set of training environments, the system generates a summary performance metric for the candidate reinforcement learning algorithm by combining the environment-specific performance metrics generated for the set of training environments. After evaluating each of the candidate reinforcement learning algorithms in the sequence, the system selects one or more output reinforcement learning algorithms from the sequence based on the summary performance metrics of the candidate reinforcement learning algorithms.