Asynchronous Reinforcement Learning with Auxiliary Terminal Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep reinforcement learning (DRL) faces challenges such as sample inefficiency and convergence to locally optimal policies, particularly in domains with sparse, delayed, or noisy rewards, where traditional methods require long training times and are hindered by the need for extensive simulation-based rollouts.
Innovation Solution
The integration of auxiliary tasks like Terminal Prediction and Planner Imitation within asynchronous distributed DRL methods, which enable faster learning by providing denser training signals and improving exploration efficiency, allowing for simultaneous execution of search and neural network components, and using lightweight MCTS as a demonstrator to guide model-free RL.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pure planning methods such as Monte Carlo tree search are used, then no offline training phase is needed, but computationally costly simulation based rollouts are required to find the best action
Solution Approach 1:
The patent performs preliminary training of neural network policies offline using reinforcement learning before deployment. This preliminary action creates pre-trained policy networks that can be quickly executed during runtime without requiring costly simulation rollouts, thus resolving the contradiction between ease of deployment and training time.
Solution Approach 2:
The patent introduces neural network policies as an intermediary between the environment and the decision-making process. These networks serve as mediators that approximate the results of expensive Monte Carlo tree search rollouts, providing fast action selection without requiring the full computational expense of simulation-based methods during deployment.
2Adaptability or versatility
If model-free reinforcement learning is used, then policy learning is enabled, but data hungriness is aggravated when reward signals are sparse, delayed, or noisy
Solution Approach 1:
The patent introduces auxiliary tasks as intermediary learning objectives that provide additional training signals. These auxiliary tasks (such as predicting terminal states, planning outcomes, or environment dynamics) serve as mediators that generate denser learning signals from the same interaction data, reducing the amount of data needed for effective policy learning in sparse reward environments.
Solution Approach 2:
The patent makes the neural network perform multiple functions simultaneously: the primary policy learning task and auxiliary tasks (terminal state prediction, planning imitation, dynamics modeling). This multi-functionality allows the same data to serve multiple learning purposes, effectively reducing data hunger by extracting more learning signals from each interaction.
3Productivity
If auxiliary tasks are added to provide denser training signals, then learning efficiency is improved, but network complexity increases
Solution Approach 1:
The patent merges multiple tasks (policy learning, terminal state prediction, planning imitation, dynamics modeling) into a single unified neural network architecture. By combining these functions in one network with shared representations, the patent improves learning efficiency through auxiliary tasks while avoiding the complexity overhead of separate networks for each function.
Solution Approach 2:
The patent designs a universal neural network that performs multiple functions: the main policy network and auxiliary networks for terminal state prediction, planning imitation, and environment dynamics modeling. This multi-functional architecture allows the system to benefit from auxiliary tasks for faster learning while managing complexity through shared computational resources and coordinated training.
Data Source
AI summary
A computer system and method for extending parallelized asynchronous reinforcement learning for training a neural network is described in various embodiments, through coordinated operation of plurality of hardware processors or threads such that each functions as a worker agent that is configured to simultaneously interact with a target computing environment for local gradient computation based on a loss determination and to update global network parameters based at least on local gradient computation to train the neural network through modifications of weighted interconnections between interconnected computing units as gradient computation is conducted across a plurality of iterations of a target computing environment, the loss determination including at least a policy loss term (actor), a value loss term (critic), and an auxiliary control loss. Variations are described further where the neural network is adapted to include terminal state prediction and action guidance.