Multi-Task Reinforcement Learning Without Replay or Target Networks
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Solution Overview
Problem
Reinforcement learning systems face inefficiencies in training neural networks for multiple tasks, requiring significant computational resources and time, and often necessitate the use of target networks and experience replay.
Innovation Solution
A distributed training system that uses an action selection neural network to learn from multiple parallel streams of experience, leveraging similarities between tasks and eliminating the need for target networks or experience replay, thereby reducing computational complexity and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems train neural networks for multiple tasks using conventional methods, then the tasks can be learned, but significant computational resources and time are required
Solution Approach 1:
The patent merges multiple task training into a single unified neural network training process. The system combines trajectories from multiple tasks into a single training dataset, allowing the network to learn multiple tasks simultaneously rather than training separate networks for each task, thereby reducing overall computational resource consumption.
Solution Approach 2:
The patent creates a universal neural network that can perform multiple different tasks. The same neural network architecture and parameters are used across all tasks, making the system multi-functional. This universal approach eliminates the need for separate target networks and experience replay mechanisms for each task, reducing computational overhead.
2Adaptability or versatility
If reinforcement learning systems train neural networks for multiple tasks, then the tasks can be learned, but training time is significantly increased
Solution Approach 1:
The patent enables continuous learning across multiple tasks by maintaining a single evolving neural network that continuously processes trajectories from all tasks. Unlike conventional methods that require periodic updates with target networks, this system continuously learns from incoming trajectories, eliminating idle time and maintaining constant productive learning across all tasks simultaneously.
Solution Approach 2:
The patent merges the training processes of multiple tasks into a single continuous training loop. By combining trajectories from all tasks into one unified training process, the system eliminates the sequential training overhead and achieves parallel learning across all tasks, significantly reducing total training time.
3Reliability
If target networks and experience replay are used for training, then training stability can be achieved, but computational complexity and resource consumption increase
Solution Approach 1:
The patent extracts and removes the unnecessary components of target networks and experience replay from the training system. By demonstrating that these components are not needed when using the proposed trajectory combination approach, the system achieves training stability with a simpler architecture, eliminating the computational complexity associated with maintaining and updating target networks and managing experience replay buffers.
Data Source
AI summary
A method of training an action selection neural network for controlling an agent interacting with an environment to perform different tasks is described. The method includes obtaining a first trajectory of transitions generated while the agent was performing an episode of the first task from multiple tasks; and training the action selection neural network on the first trajectory to adjust the control policies for the multiple tasks. The training includes, for each transition in the first trajectory: generating respective policy outputs for the initial observation in the transition for each task in a subset of tasks that includes the first task and one other task; generating respective target policy outputs for each task using the reward in the transition, and determining an update to the current parameter values based on, for each task, a gradient of a loss between the policy output and the target policy output for the task.