Robot Control With Hindsight Replay for Multi-Target Training
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Solution Overview
Problem
Existing robot control methods require extensive training for each feature task, suffer from poor generalization, and fail to effectively utilize hindsight and foresight experience replay, leading to low training efficiency and accuracy.
Innovation Solution
A robot control method incorporating multi-target reinforcement learning with hindsight and foresight experience replay, utilizing environment interaction data including state, action, reward, and target values to accelerate training and improve generalization by leveraging failure data and multi-step reward expansion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reinforcement learning control algorithms are used for robot control, then the robot can learn autonomously to complete specified tasks, but the training process is slow and the model has poor generalization capability
Solution Approach 1:
The patent applies preliminary action by pre-processing environment interaction data to generate expanded training datasets before actual training. The data expansion process creates multiple augmented samples from limited original data, preparing a richer training corpus in advance to accelerate subsequent model training and improve generalization capability
Solution Approach 2:
The patent uses copying by creating multiple augmented copies of the original training data through data expansion techniques. Each original interaction data point generates multiple augmented samples with varied transformations, allowing the model to learn from diverse data representations without requiring additional physical training time
2Manufacturing precision
If separate models are trained for each feature task, then the model can achieve good performance on specific tasks, but the overall system complexity increases and generalization deteriorates
Solution Approach 1:
The patent implements universality by training a single multi-functional robot control model that can handle multiple different tasks. The model is designed with universal features that enable it to perform various robot control tasks without requiring separate specialized models for each task, thereby reducing system complexity while maintaining task execution accuracy
Solution Approach 2:
The patent applies merging by combining multiple task-specific training processes into a single unified training framework. The environment interaction data from various tasks are integrated and processed together to train one comprehensive model, eliminating the need to maintain and coordinate multiple separate models
3Stability of the object's composition
If traditional experience replay mechanisms are used, then the training process can be stabilized, but the data utilization rate remains low and training efficiency is insufficient
Solution Approach 1:
The patent applies parameter changes by transforming the original experience replay parameters and data structures to enable more efficient data utilization. The data expansion process modifies how training data is structured and sampled, allowing the model to learn from augmented datasets while maintaining training stability through controlled parameter adjustments
Solution Approach 2:
The patent implements continuity of useful action by ensuring that the data expansion process continuously generates valuable training samples throughout the training process. The augmented data provides ongoing useful information that maintains training stability while improving efficiency, allowing the learning process to continue effectively without interruptions or redundant operations
Data Source
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AI summary
A robot control method, relating to the teclmical field of artificial intelligence. The method comprises: acquiring environmental interaction data and an actual target value which is actually achieved after an action corresponding to action data in the environmental interaction data is executed; according to state data, action data and an actual target value at a first moment among two adjacent moments, determining a reward value after the action is executed; updating a reward value in the environmental interaction data by using the reward value after the action is executed; training, by using the updated environmental interaction data, an intelligent agent corresponding to a robot control network; and controlling an action of a target robot by using the trained intelligent agent. Further disclosed are a robot control apparatus and device, and a computer storage medium and a computer program product.