Global Policy Neural Network Training for Asynchronous Robot Learning
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Solution Overview
Problem
Existing robotic systems face challenges in learning complex feedback control policies for manipulation tasks with discontinuous contact dynamics, particularly when dealing with high-dimensional sensory inputs, as they struggle to effectively train global policy neural networks across multiple asynchronous workers without compromising system performance.
Innovation Solution
A distributed training system is implemented, utilizing multiple local and global workers that operate asynchronously to train a global policy neural network, where local workers learn simple policy controllers and generate trajectories, while global workers supervise the training of the network using optimized trajectories, effectively leveraging increased data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a distributed training system uses multiple asynchronous workers to train a global policy neural network, then training efficiency and data utilization are improved, but system complexity and coordination difficulty increase
Solution Approach 1:
The training system is segmented into multiple independent workers (local and global) that operate asynchronously. Each worker handles specific training tasks independently, allowing parallel processing and improved training efficiency without requiring complex synchronization mechanisms.
Solution Approach 2:
A parameter server acts as an intermediary between local workers and global workers. It manages parameter updates and coordinates communication, simplifying the complexity of direct worker-to-worker interactions while maintaining distributed training efficiency.
2Measurement precision
If high-dimensional sensory inputs are used for robotic tasks, then action selection accuracy is improved, but training difficulty and computational load increase
Solution Approach 1:
The neural network training is segmented into local training (on individual robots) and global training (across all robots). Local workers process high-dimensional sensory inputs independently, reducing the immediate computational burden on any single system while maintaining accuracy through distributed learning.
Solution Approach 2:
The system transitions from single-robot training to multi-robot distributed training, adding a temporal and spatial dimension to the learning process. This allows the system to handle high-dimensional inputs more effectively by distributing computational load across multiple processors operating in parallel.
3Productivity
If asynchronous training is implemented across multiple workers, then training speed is improved, but policy consistency and reliability deteriorate
Solution Approach 1:
The parameter server implements a feedback mechanism where global workers periodically update parameters based on collected experience data. This feedback loop ensures that asynchronous workers remain aligned with the global policy, maintaining consistency while allowing parallel training to proceed at high speed.
Solution Approach 2:
Local workers perform preliminary training actions independently before global parameter updates are applied. This allows training to proceed asynchronously at high speed while the preliminary actions ensure that local policies remain valid until the next global update, maintaining reliability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing an instance of the robotic task for multiple local workers, generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task, optimizing a local policy controller on the trajectory, generating an optimized trajectory using the optimized local controller, and storing the optimized trajectory in a replay memory associated with the local worker. The method includes sampling, for multiple global workers, an optimized trajectory from one of one or more replay memories associated with the global worker, and training the replica of the global policy neural network maintained by the global worker on the sampled optimized trajectory to determine delta values for the parameters of the global policy neural network.