Agent Behavior Training via User-Defined Reward Tokens
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Solution Overview
Problem
Existing methods for training agent behavior in specific environments are complex and require expertise, as they involve creating handcrafted rules or using reinforcement learning, making it difficult for lay users to deploy agents effectively in particular environments without causing damage or inefficiency.
Innovation Solution
An apparatus using reinforcement learning that allows users to position reward tokens in the environment, enabling agents to learn and update their behavior policies based on observations and actions, without needing to understand the underlying reinforcement learning mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If handcrafted rules or reinforcement learning are used to define behavior policy, then agent behavior can be controlled, but the complexity of implementation increases and requires expert programming knowledge
Solution Approach 1:
The patent introduces a behavior policy that serves as an intermediary layer between the agent and the environment. This behavior policy abstracts the complex reinforcement learning mechanisms from the end user, allowing users to define high-level behavioral rules without needing to understand the underlying complex algorithms. The behavior policy mediates between simple user instructions and the complex reinforcement learning engine.
Solution Approach 2:
The system enables self-service by allowing the agent to automatically learn and optimize its behavior through reinforcement learning based on rewards provided by end users. Once end users provide simple reward signals, the agent autonomously improves its behavior through the reinforcement learning process without requiring continuous expert intervention or complex manual programming.
2Reliability
If handcrafted rules are used to define behavior policy, then agent behavior can be precisely controlled, but it becomes difficult to deploy agents in specific environments without causing damage or inefficiency
Solution Approach 1:
The agent performs self-learning through reinforcement learning, automatically adapting to specific environments by receiving reward signals from end users. This eliminates the need for experts to manually program environment-specific behaviors, allowing lay users to easily deploy agents in various environments by simply providing reward feedback.
Solution Approach 2:
The system changes the parameters of behavior control from fixed handcrafted rules to dynamic parameters learned through reinforcement learning. The behavior policy parameters are continuously adjusted based on reward signals from end users, enabling the agent to adapt to different environments while maintaining precise control over its actions.
3Adaptability or versatility
If reinforcement learning is used to update behavior policy, then agent adaptability improves, but the requirement for machine learning expertise increases
Solution Approach 1:
The behavior policy acts as an intermediary that shields end users from the complexity of reinforcement learning mechanisms. Users interact with the system through simple reward signals rather than needing to understand or configure complex reinforcement learning parameters, while the agent handles the sophisticated learning processes in the background.
Solution Approach 2:
The agent autonomously performs the complex reinforcement learning updates and adaptations without requiring user expertise. Once end users provide simple reward feedback, the agent independently manages the entire reinforcement learning process, including policy updates and parameter optimization, eliminating the need for machine learning expertise from users.
Data Source
AI summary
An apparatus is described for training a behavior of an agent in a physical or digital environment. The apparatus comprises a memory storing the location of at least one reward token in the environment. The location has been specified by a user. At least one processor executes the agent in the environment according to a behavior policy. The processor is configured to observe values of variables comprising: an observation of the agent, an action of the agent and any reward resulting from the reward token. The processor is configured to update the behavior policy using reinforcement learning according to the observed values.