Reinforcement Learning Action Selection via Goal Observation Merging
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Solution Overview
Problem
Reinforcement learning agents face challenges in selecting effective actions in real-world environments, particularly when encountering target states not experienced during learning, and struggle to leverage the shared structure between goal and observation spaces.
Innovation Solution
A reinforcement learning system utilizing neural networks to generate combined representations of goals and observations, with shared parameters between goal and observation neural networks, and an action score neural network to select actions based on these representations, enabling effective action selection in real-world environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reinforcement learning agents use neural networks to select actions based on observations, then they can learn from experience, but they fail to effectively consider target states (goals) when selecting actions in real-world environments
Solution Approach 1:
The patent merges the goal processing capability and observation processing capability into a unified neural network architecture. The goal neural network and observation neural network are combined to jointly determine action scores, allowing the system to effectively consider both target states and current observations when selecting actions, thereby resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The patent creates a multi-functional neural network system where the same architecture can process both goals and observations. The goal neural network and observation neural network share similar structures and can be trained independently or jointly, providing universal action selection capability for different types of inputs while maintaining manageable complexity through parameter sharing and modular design.
2Adaptability or versatility
If reinforcement learning agents are trained on specific goals during learning, then they can perform well on those goals, but they struggle to select actions for unseen goals that were not encountered during learning
Solution Approach 1:
The patent designs a universal neural network architecture that can handle both seen and unseen goals. By training the goal neural network on multiple goals during learning and using the same architecture for observation processing, the system develops generalized capabilities to select appropriate actions for novel goals while maintaining reliable performance on familiar goals through the shared structural framework.
Solution Approach 2:
The system uses feedback from the interaction between goal representations and observation representations to improve action selection. The joint processing of goals and observations through the neural network provides feedback mechanisms that help the agent learn to generalize to unseen goals while maintaining performance consistency through continuous learning from environmental interactions.
3Loss of information
If the system uses separate neural networks for goals and observations, then it can process both types of information, but it fails to leverage the shared structure between the space of goals and the space of observations
Solution Approach 1:
The patent merges the processing of goals and observations by allowing the goal neural network and observation neural network to jointly determine action scores. This integration enables the system to leverage the shared structure between goal space and observation space, as both inputs are processed through compatible neural network architectures that can identify and utilize common patterns, thereby reducing information loss without significantly increasing complexity through parameter sharing.
Data Source
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AI summary
Reinforcement learning using goals and observations selects actions to be performed by a reinforcement learning agent interacting with an environment. In the embodiments, the agent interacts with the environment to attempt to reach a predetermined set of target states of the environment. When the environment is a real-world environment and the agent is a robot interacting with the environment to accomplish a specific task, each target state can be a state in which a subtask has been completed by the robot, e.g., when the robot has moved an item from one location to another or has otherwise made progress toward completing the task. One of the methods includes receiving an observation characterizing a current state of the environment; receiving a goal characterizing a target state from a set of target states of the environment; processing the observation using an observation neural network to generate a numeric representation of the observation; processing the goal using a goal neural network to generate a numeric representation of the goal; combining the numeric representation of the observation and the numeric representation of the goal to generate a combined representation; processing the combined representation using an action score neural network to generate a respective score for each action in the predetermined set of actions; and selecting the action to be performed using the respective scores for the actions in the predetermined set of actions.