Robot Control With Discriminator-Guided Strategy Learning
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Solution Overview
Problem
Current methods for training models to imitate expert behavior in reinforcement learning are inefficient and lack effective adaptation to changing strategies, leading to suboptimal learning and control in complex environments.
Innovation Solution
A computer-implemented method that trains a parametric model using a discriminator to determine rewards based on expert and model trajectories, allowing for parameterization through gradient descent, and alternating training steps between the discriminator and model, enabling efficient learning of long-term behavior and strategy adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reinforcement learning methods are used to train models, then the training process is simple, but the learning efficiency is low and the model cannot adapt to changing strategies effectively
Solution Approach 1:
The patent implements dynamic training by alternating between discriminator training and model training in iterative cycles. The discriminator is trained first to evaluate trajectories, then the model uses the discriminator's feedback to update its policy, creating a dynamic adaptation loop that continuously improves strategy performance while maintaining training efficiency.
Solution Approach 2:
The discriminator provides feedback signals to the model by evaluating trajectories and generating reward values. This feedback mechanism allows the model to learn from the discriminator's assessment of expert versus model trajectories, enabling effective strategy adaptation without requiring complex retraining processes.
2Measurement precision
If the model is trained to closely match expert behavior, then the behavior imitation accuracy is high, but the training complexity and computational cost increase
Solution Approach 1:
The discriminator serves as an intermediary between the expert trajectories and the model trajectories. Instead of directly comparing complex behavior patterns, the discriminator mediates the learning process by providing simplified reward signals that guide the model toward expert-like behavior, reducing training complexity while maintaining high imitation accuracy.
Solution Approach 2:
The training process is segmented into distinct phases: expert trajectory collection, discriminator training, and model training. This segmentation allows each component to be optimized independently, reducing overall training complexity while achieving high behavior imitation accuracy through focused learning at each stage.
3Reliability
If long-term behavior prediction is improved, then the model accuracy for complex environments is high, but the computational resources and training time increase
Solution Approach 1:
The discriminator is trained in advance to establish a robust evaluation framework before model training begins. This preliminary action creates a ready-made feedback mechanism that accelerates subsequent model training, allowing the model to quickly learn long-term behavior patterns without extensive computational resources during the actual training phase.
Solution Approach 2:
The training process uses periodic alternating updates between the discriminator and model. Instead of continuous complex computations, the system performs periodic training cycles where each component is updated in turns, reducing overall training time while maintaining high long-term prediction accuracy through repeated refinement.
Data Source
AI summary
A computer-implemented method for applying control to a robot, and apparatus therefor. A parametric model of an environment, in particular a deep neural network, is trained in accordance with a method for training the parametric model of the environment. The model is trained depending on a controlled system. A strategy is learned in accordance with a method for model-based learning of the strategy. Control is applied to the robot depending on the parametric model and on the strategy.