Reward Function for Neural Network Exploration Training
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Solution Overview
Problem
Current exploration strategies in reinforcement learning lack incentive for agents to explore novel situations, leading to limited diversity in behaviors and inadequate training of neural networks in dynamic environments.
Innovation Solution
Implementing a reward-based strategy that differentiates between novel and previously explored agent states, using a neural network to process simulations and assign rewards for unexplored states, thereby promoting exploration and expanding the agent's knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reinforcement learning is used without reward-based exploration strategies, then the training process is simpler, but the agent fails to explore novel situations leading to limited behavioral diversity
Solution Approach 1:
The patent implements a feedback mechanism where the agent receives rewards based on whether it transitions to novel or previously explored states. This feedback loop encourages exploration by providing positive reinforcement for discovering new states, thereby increasing behavioral diversity without requiring complex manual intervention
Solution Approach 2:
The system enables self-service exploration by automatically identifying novel states and assigning rewards without external intervention. The neural network autonomously determines which states are novel and provides appropriate rewards, allowing the agent to self-drive the exploration process
2Adaptability or versatility
If exploration strategies are implemented to maximize diversity, then behavioral diversity improves, but the incentive mechanism for exploring novel situations becomes insufficient
Solution Approach 1:
The patent replaces manual or mechanical methods of identifying novel states with a neural network-based system. The neural network automatically processes state information and determines novelty, providing accurate identification of new states while enabling the reward mechanism to effectively incentivize exploration
3Loss of information
If the neural network is trained with novel unexplored agent states, then the knowledge base expands, but the training data requirements increase
Solution Approach 1:
The system performs preliminary identification of novel states during the exploration process and prepares training data incrementally. By identifying and storing novel state information as it encounters them during exploration, the system builds the knowledge base progressively without requiring large batches of pre-collected training data
Data Source
AI summary
A system and method for implementing reward based strategies for promoting exploration that include receiving data associated with an agent environment of an ego agent and a target agent and receiving data associated with a dynamic operation of the ego agent and the target agent within the agent environment. The system and method also include implementing a reward function that is associated with exploration of at least one agent state within the agent environment. The system and method further include training a neural network with a novel unexplored agent state.