Distributed Prioritized Replay for Scalable Reinforcement Learning
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Solution Overview
Problem
Existing reinforcement learning systems face inefficiencies in training neural networks due to limited scalability and ineffective prioritization of experience data, leading to suboptimal learning speed and performance, especially in real-world applications.
Innovation Solution
A distributed architecture with multiple actor computing units interacting with environments and a shared experience replay memory, combined with learner computing units that prioritize and update the neural network using prioritized experience tuples, allowing for faster and more efficient training across multiple computing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple actors generate experience tuples in parallel, then training data generation speed increases, but system complexity and coordination overhead increase
Solution Approach 1:
The system divides the reinforcement learning process into separate actor and learner components that operate independently. Multiple actors generate experience tuples in parallel without direct coordination, while learners process these tuples asynchronously. This segmentation eliminates the need for complex synchronization mechanisms while maintaining high parallelism in data generation.
Solution Approach 2:
The shared replay memory serves as an intermediary buffer between actors and learners. Actors deposit experience tuples into this shared memory without direct interaction with learners, and learners retrieve tuples asynchronously. This intermediary structure decouples the parallel actors from the learning process, enabling high-speed data generation without coordination overhead.
2Productivity
If experience tuples are prioritized based on temporal difference error, then learning efficiency improves, but computational overhead increases
Solution Approach 1:
The system computes temporal difference errors and assigns priorities to experience tuples at the time of generation, before the learning phase begins. This preliminary prioritization allows learners to efficiently sample high-value tuples without performing additional complex computations during the learning process, shifting the computational burden to the data generation phase.
Solution Approach 2:
Each actor independently computes its own experience tuples and prioritizes them based on local temporal difference errors without requiring centralized coordination or additional computational resources from other system components. This self-service approach minimizes overall computational overhead while achieving effective prioritization.
3Adaptability or versatility
If a shared replay memory is used by multiple actors and learners, then data diversity increases, but memory access contention increases
Solution Approach 1:
The system maintains continuous operation of both actors and learners without synchronization barriers. Actors continuously generate and deposit experience tuples into the shared replay memory while learners continuously sample and process tuples in parallel. This continuous operation maximizes data diversity generation while minimizing idle time, effectively masking memory access contention through sustained computational activity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. One of the systems includes (i) a plurality of actor computing units, in which each of the actor computing units is configured to maintain a respective replica of the action selection neural network and to perform a plurality of actor operations, and (ii) one or more learner computing units, in which each of the one or more learner computing units is configured to perform a plurality of learner operations.