Robot Controller Training With Demonstrations and Actor-Critic RL
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Solution Overview
Problem
Existing robot control methods using reinforcement learning face inefficiencies in data usage and training time due to high sample complexity and overestimation of action-value functions, often resulting in unsafe or unacceptable behaviors.
Innovation Solution
A method that utilizes pre-knowledge from demonstrations, such as expert or previously designed controllers, to pre-train actor and critic neural networks through imitation and supervised learning, followed by reinforcement learning with delayed updates and prioritized experience replay to improve control policies efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model-free actor-critic reinforcement learning is used to control robot devices, then the controller can learn sequential decision-making policies, but the training requires high sample complexity and extensive data samples
Solution Approach 1:
The patent applies preliminary action by pre-training the actor neural network through imitation learning using demonstrations before performing reinforcement learning. This preliminary training phase provides the actor with initial knowledge about useful actions, reducing the sample complexity required during subsequent reinforcement learning training.
Solution Approach 2:
The patent introduces an intermediary critic neural network that evaluates actions and provides feedback to the actor. The critic is trained through supervised learning to predict returns, serving as a mediator that guides the actor's learning process and reduces exploration requirements.
2Adaptability or versatility
If reinforcement learning with exploration is performed, then the controller can discover new policies and states, but the training time increases significantly
Solution Approach 1:
The patent performs preliminary imitation learning training before reinforcement learning to provide the actor with initial policy knowledge. This reduces the time needed for exploration during reinforcement learning while still allowing policy improvement.
Solution Approach 2:
The patent implements feedback through the critic neural network that evaluates actor actions and provides return predictions. This feedback mechanism guides the actor's policy updates more efficiently, reducing the time required to discover improved policies compared to pure exploration-based methods.
3Adaptability or versatility
If the actor neural network performs extensive exploration, then it can find better policies, but unsafe or unacceptable behaviors occur during training
Solution Approach 1:
The patent pre-trains the actor through imitation learning from demonstrations before reinforcement learning. This preliminary phase establishes a baseline of safe and acceptable behaviors, constraining subsequent exploration to remain closer to demonstrated safe policies.
Solution Approach 2:
The critic neural network acts as an intermediary that evaluates the safety and quality of actor actions through return predictions. This feedback mechanism allows the patent to constrain exploration to safe regions of the action space while still enabling policy optimization.
4Adaptability or versatility
If reinforcement learning is used to train robot controllers, then adaptive control policies can be learned, but the training data requirements are high
Solution Approach 1:
The patent performs preliminary imitation learning training using demonstrations before reinforcement learning. This preliminary phase provides the actor with initial adaptive control knowledge from expert demonstrations, reducing the amount of training data required during reinforcement learning.
Solution Approach 2:
The critic neural network serves as an intermediary that provides return predictions to guide the actor's learning. This allows the system to learn adaptive control policies more efficiently by following the critic's guidance rather than requiring extensive trial-and-error data collection.
Data Source
AI summary
A method for controlling a robot device. The method includes performing an initial training of an actor neural network by imitation learning of demonstrations, controlling the robot device by the initially trained actor neural network to generate multiple trajectories, wherein each trajectory comprises a sequence of actions selected by the initially actor neural network in a sequence of states, and observing the return for each of the selected actions, performing an initial training of a critic neural network by supervised learning, wherein the critic neural network is trained to determine the observed returns of the actions selected by the initially actor neural network, training the actor neural network and the critic neural network by reinforcement learning starting from the initially trained actor neural network and the initially trained critic neural network and controlling the robot device by the trained actor neural network and trained critic neural network.