Environment Model Parameterization for Agent Decision-Making
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Solution Overview
Problem
Existing systems face challenges in efficiently controlling agents interacting with environments due to the need to balance exploration and exploitation in decision-making under uncertainty, particularly when navigating differences between target and source environments.
Innovation Solution
A system that uses an environment model parameterized by interaction data from both the target and source environments to select actions, sampling current values of environment model parameters based on a probability distribution derived from interaction history data, and generating expected rewards for actions to optimize decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the agent explores the target environment directly to learn reward distributions, then the accuracy of decision-making improves, but the time and resources required increase significantly
Solution Approach 1:
The system performs preliminary learning in source environments before the agent interacts with the target environment. Environment models are trained on source environments first, capturing reward distributions and dynamics. This preliminary action reduces the exploration burden when the agent later interacts with the target environment, as the pre-trained models provide initial guidance and reduce uncertainty.
Solution Approach 2:
The system creates environment models that are copies or representations of the target environment based on data from source environments. These models replicate the reward distributions and dynamics without requiring direct extensive interaction with the actual target environment. The agent can then interact with these copied models to learn decision-making patterns that transfer to the real environment.
2Reliability
If the agent interacts extensively with the target environment to learn its dynamics, then the reliability of the environment model improves, but the cost of interactions and resources increases
Solution Approach 1:
The system merges data from multiple source environments with the target environment to train the environment model. By combining interactions from source environments (which may be cheaper or safer to interact with) and limited target environment interactions, the system achieves reliable model training without requiring extensive costly target environment interactions. The merged data provides robust learning while reducing overall interaction costs.
Solution Approach 2:
The environment model serves multiple functions: it predicts reward distributions, guides agent exploration, and reduces the need for direct target environment interactions. By making the model multi-functional, the system achieves high reliability without proportionally increasing interaction costs, as the same model structure handles multiple decision-making tasks across different environments.
3Measurement precision
If the system uses only target environment data for model training, then the model accuracy for the target environment improves, but the sample efficiency decreases
Solution Approach 1:
The system introduces source environments as intermediaries between the agent and the target environment. Instead of directly using only target environment data, the system uses source environments to generate training data that mediates the learning process. This intermediary approach allows the model to learn generalizable patterns from source environments that transfer to the target environment, improving sample efficiency while maintaining accuracy.
Solution Approach 2:
The system changes the parameters or conditions of the training data by incorporating data from source environments with different characteristics. By adjusting the data sources and their weighting, the system achieves better sample efficiency. The model learns from diverse parameter settings in source environments, which improves its ability to generalize to the target environment with fewer samples.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions for an agent in a target environment. In particular, the actions are selected using an environment model for the target environment that is parameterized using interactions of the agent with the target environment and one or more source environments.