Scene Grammar Generation for Behavior-Matched Agent Training
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Solution Overview
Problem
Reinforcement learning is a tedious process, and end users lack the time, resources, and expertise to manually generate various environments to achieve human-like agent behavior, which is difficult to learn in specific environments.
Innovation Solution
A method involving scene grammars is used to generate environments that induce particular behaviors in reinforcement learning agents, where environments are selected based on similarity to a reference agent's behavior, and the system automatically identifies and stores effective environments for training new agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual environment generation is used to train agents, then agent behavior can be customized, but the time and expertise required increase significantly
Solution Approach 1:
The system creates virtual environments that replicate real-world scenarios where agents can practice behaviors. These virtual environments copy the essential characteristics of real-world situations (e.g., circular room for walking, flat ground with no walls) without requiring manual setup by users, thus making agent training easier and faster
Solution Approach 2:
The system introduces an intermediary layer between the user's training goals and the actual environment setup. This intermediary automatically generates appropriate environments based on desired behaviors, acting as a mediator that translates high-level training objectives into concrete environmental configurations without requiring user expertise
2Reliability
If complex behaviors are taught through reinforcement learning, then human-like behavior is achieved, but the process becomes tedious and resource-intensive
Solution Approach 1:
The training process is segmented into distinct phases: environment generation, behavior evaluation, and agent training. The system divides the complex task of teaching human-like behavior into manageable components, where each phase handles a specific aspect of the training process, reducing overall complexity while maintaining behavior accuracy
Solution Approach 2:
The system changes key parameters of the training environment (such as creating circular rooms vs. flat open spaces) to induce specific behaviors in agents. By systematically varying environmental parameters rather than manually configuring every detail, the system achieves reliable behavior training with reduced process complexity
3Reliability
If multiple environments are generated for training, then behavior learning is improved, but the resources required increase
Solution Approach 1:
The system generates environments that serve multiple training purposes simultaneously. A single virtual environment can be used to train multiple aspects of agent behavior or can be reused across different training runs, making the environments multi-functional and reducing the total number of environments needed while maintaining learning effectiveness
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning in agent training. Agents may be trained using reinforcement learning. The methods, systems, and apparatus include actions of obtaining scene grammars, obtaining a reference video of a reference agent performing a task, generating environments from the scene grammars, determining that behavior of a sample agent shown in a particular environment of the environments matches behavior of the reference agent, storing an indication that the particular environment trains agents to perform the task, determining to train a new agent to perform the task, identifying the particular environment based on the indication, and training the new agent to perform the task in the particular environments identified.