Action Selection Neural Network Training with Exploration Rewards
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Solution Overview
Problem
Existing machine learning systems face challenges in effectively training action selection neural networks to balance exploration and exploitation, especially in complex environments with sparse task rewards, and struggle to efficiently learn policies that maximize cumulative task rewards.
Innovation Solution
The system implements a method for training an action selection neural network using a reinforcement learning technique that incorporates an exploration importance factor to balance exploration and exploitation, generating exploration rewards based on similarity measures between current and previous observations, and uses a Retrace double Q-learning technique with prioritized experience replay to optimize action selection policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the agent uses standard reinforcement learning to train action selection policies, then it can learn to maximize cumulative task rewards, but it struggles to balance exploration and exploitation effectively in complex environments with sparse rewards
Solution Approach 1:
The patent segments the reward signal into two distinct components: task reward (for goal completion) and exploration reward (for visiting novel states). This segmentation allows the agent to independently optimize for task completion while simultaneously encouraging exploration of novel environments, resolving the balance between exploitation and exploration in sparse reward settings.
Solution Approach 2:
The patent introduces an exploration reward as an intermediary mechanism that mediates between the agent's exploration actions and the sparse task rewards. This intermediary exploration reward signal enables the agent to learn effective policies by rewarding exploratory behavior itself, rather than waiting for rare task successes, thus accelerating learning in complex environments.
2Adaptability or versatility
If the agent explores the environment to discover new states, then it can potentially find better actions, but it wastes time and computational resources when exploration is insufficiently guided
Solution Approach 1:
The patent changes the parameter of reward assignment by introducing exploration importance factors that modulate the exploration reward based on state novelty. This parameter change enables the system to dynamically adjust exploration incentives - providing higher rewards for truly novel states while reducing rewards for redundant explorations, thus optimizing the balance between adaptability and training efficiency.
3Reliability
If the agent relies solely on task rewards for training, then the learning objective is simple, but the agent fails to learn effective policies in environments with sparse or delayed rewards
Solution Approach 1:
The patent ensures continuity of useful action by providing continuous exploration rewards throughout the training process, rather than relying on intermittent task rewards. This continuous reward signal maintains learning momentum and guides the agent to consistently explore and learn effective policies, even in environments where task rewards are sparse or delayed.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network that is used to select actions to be performed by an agent interacting with an environment. In one aspect, the method comprises: receiving an observation characterizing a current state of the environment; processing the observation and an exploration importance factor using the action selection neural network to generate an action selection output; selecting an action to be performed by the agent using the action selection output; determining an exploration reward; determining an overall reward based on: (i) the exploration importance factor, and (ii) the exploration reward; and training the action selection neural network using a reinforcement learning technique based on the overall reward.