Robot Control With Multi-Target Reinforcement Learning Replay
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Solution Overview
Problem
Existing robot control methods require extensive training for each feature task, suffer from poor generalization, and have slow training processes, limiting their efficiency in handling multiple targets and accelerating agent training.
Innovation Solution
A multi-target reinforcement learning robot control method incorporating foresight and hindsight experience replay, which enables simultaneous training of multiple targets, improves data utilization efficiency, and accelerates the training process by leveraging both foresight and hindsight experience replay techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reinforcement learning methods are used for robot control, then the robot can learn to complete specific tasks, but the training process is slow and requires extensive training for each feature task
Solution Approach 1:
The patent applies preliminary action by pre-processing environment interaction data to extract state data, action data, and return values before training. This preprocessing step prepares the data in advance, allowing the reinforcement learning model to train faster without compromising task completion capability. The state data includes position, orientation, and other environmental parameters that are prepared beforehand for efficient training.
Solution Approach 2:
The patent uses copying by creating a simplified representation of the environment through state data that captures essential features (position, orientation, etc.) without requiring the full complexity of the original environment. This copied state representation allows faster training while maintaining the ability to complete tasks effectively.
2Reliability
If traditional reinforcement learning methods are used, then the robot can be trained for specific tasks, but the model has poor generalization to new tasks
Solution Approach 1:
The patent implements universality by designing a unified reinforcement learning framework that handles multiple task types (navigation, manipulation, etc.) through a single model architecture. The state data structure and return value calculations are designed to be task-agnostic, allowing the same model to generalize across different tasks by simply changing the task specification rather than retraining the entire system.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting task-specific parameters (target positions, object properties, task goals) while keeping the core model architecture unchanged. This allows the model to adapt to new tasks by changing parameters rather than structural components, improving generalization while maintaining task completion accuracy.
3Reliability
If extensive training is performed for each feature task, then task-specific performance is improved, but data utilization efficiency is low and training resources are wasted
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing environment interaction data that can be reused across multiple tasks. Instead of generating new training data for each task, the system prepares a comprehensive dataset in advance that covers various states and actions, reducing redundant data generation and improving training resource efficiency.
Solution Approach 2:
The patent implements discarding and recovering by reusing environment interaction data across multiple training iterations and tasks. Rather than discarding data after a single use, the system recycles interaction data (states, actions, return values) for multiple training purposes, significantly improving data utilization efficiency and reducing waste of training resources.
Data Source
AI summary
Embodiments of the disclosure provide a robot control method, apparatus and device, a computer storage medium and a computer program product and relate to the technical field of artificial intelligence. The method includes: acquiring environment interaction data and an actual target value, indicating a target that is actually reached by executing an action corresponding to action data in the environment interaction data; determining a return value after executing the action according to state data, action data and the actual target value at the first time of two adjacent times; updating a return value in the environment interaction data by using the return value after executing the action; training an agent corresponding to a robot control network by using the updated environment interaction data, and controlling the action of a target robot by using the trained agent.