Batched Reinforcement Learning Parallel Inference
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Solution Overview
Problem
Conventional reinforcement learning techniques are bottlenecked by the time and computational resources required to collect and process transition tuples, which slows down the training of action selection neural networks.
Innovation Solution
The system employs batched reinforcement learning by parallelizing neural network inference and environment interactions across multiple CPU cores and hardware accelerators, allowing simultaneous action selection and environment interaction across multiple environment replicas, thereby reducing training time and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional reinforcement learning techniques are used to train action selection neural networks, then the training process is sequential and thorough, but the training time and computational resources required are excessively large
Solution Approach 1:
The patent segments the training process into multiple independent environment replicas that can be trained in parallel. Each replica represents an independent training instance that collects transition tuples separately, allowing the overall training to be divided into concurrent segments rather than a single sequential process.
Solution Approach 2:
The patent merges multiple environment replicas and their training processes into a unified parallel training framework. By combining multiple sequential training instances into simultaneous parallel execution, the system achieves both thorough training quality and reduced overall training time through aggregate parallel processing.
2Reliability
If conventional reinforcement learning techniques are used to collect transition tuples, then comprehensive training data is gathered, but the computational resources and time required are excessively large
Solution Approach 1:
The patent segments the data collection process across multiple environment replicas, where each replica independently collects transition tuples in parallel. This segmentation allows comprehensive data gathering to be distributed across multiple computational streams simultaneously, reducing the temporal and resource burden on any single processing unit.
Solution Approach 2:
The patent introduces parallelism as a new dimension for data collection by spawning multiple environment replicas that operate simultaneously. Instead of collecting data sequentially in one dimension of time, the system collects data across multiple parallel dimensions, achieving comprehensive coverage with reduced computational overhead per unit time.
3Measurement precision
If sequential neural network inference is performed for action selection, then accurate action selection is achieved, but the training process is bottlenecked and computationally inefficient
Solution Approach 1:
The patent segments the neural network inference process by creating independent inference streams for multiple environment replicas. Each replica performs its own neural network inference sequentially to ensure accuracy, while the overall system achieves parallel processing efficiency by running multiple segmented inference processes simultaneously.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for batched reinforcement learning. For example, the batched reinforcement learning techniques can be used to determine a control policy for a robot in simulation and the control policy can then be used to control the robot in the real world. In one aspect, a method includes obtaining a plurality of current observations, each current observation characterizing a current state of a respective environment replica; processing the current observations in parallel using the action selection neural network in accordance with current values of the network parameters to generate an action batch; obtaining a transition tuple batch comprising a respective transition tuple for each of the environment replicas, the respective transition tuple for each environment replica comprising: (i) a subsequent observation and (ii) a reward; and training the action selection neural network on the batch of transition tuples.