Unsupervised RL Agent Reward Generation via State Embedding Similarity
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Solution Overview
Problem
Reinforcement learning systems face challenges in training agents to transition environments into specific goal states without explicit external reward signals, particularly in resource-intensive tasks like robotics, where hand-crafting rewards is difficult and time-consuming.
Innovation Solution
A system that uses an action selection neural network and an embedding neural network to generate rewards based on the similarity between current and goal states, allowing for autonomous learning and reducing computational resource consumption by leveraging reinforcement learning techniques and experience replay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-crafted reward functions are used to guide reinforcement learning, then the agent can learn to reach goal states, but the system becomes time-consuming and resource-intensive to develop and train
Solution Approach 1:
The system employs intrinsic reward functions that enable the agent to self-guide toward goal states without external reward engineering. The embedding network automatically generates reward signals based on the similarity between current and goal states, eliminating the need for manual reward function design and significantly reducing training time while maintaining reliable goal-reaching capability
Solution Approach 2:
The patent replaces the mechanical process of hand-crafting reward functions with an automated neural network-based embedding system. The embedding network learns to generate intrinsic rewards through experience replay and similarity computation, substituting the manual engineering process with an autonomous learning mechanism that reduces both development time and computational resources
2Productivity
If explicit external reward signals are provided, then the agent learns effectively, but the system complexity and resource requirements increase
Solution Approach 1:
The system extracts the reward signal generation process from external sources and embeds it within the agent's own decision-making architecture. The embedding network computes intrinsic rewards by measuring state similarity, extracting meaningful guidance signals directly from the environment dynamics rather than relying on complex external reward functions, thereby simplifying the overall system while maintaining learning effectiveness
Solution Approach 2:
The embedding network serves multiple functions simultaneously: it processes observations, generates intrinsic reward signals, and learns from experience replay. This multi-functional component replaces the need for separate reward engineering, observation processing, and learning modules, reducing system complexity while maintaining or improving learning speed across diverse tasks
3Reliability
If traditional reinforcement learning with hand-crafted rewards is used, then goal-directed learning is achieved, but computational resources are excessively consumed
Solution Approach 1:
The system changes the parameterization of reward functions from hand-crafted numerical values to continuous embeddings generated by the neural network. By representing states as embedded vectors and computing rewards based on embedding similarity, the system achieves goal-directed learning with reduced computational overhead, as the embedding space provides a compact and efficient representation that requires fewer resources to process than traditional state-space methods
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent that interacts with an environment. In one aspect, a system comprises: an action selection subsystem that selects actions to be performed by the agent using an action selection policy generated using an action selection neural network; a reward subsystem that is configured to: receive an observation characterizing a current state of the environment and an observation characterizing a goal state of the environment; generate a reward using an embedded representation of the observation characterizing the current state of the environment and an embedded representation of the observation characterizing the goal state of the environment; and a training subsystem that is configured to train the action selection neural network based on the rewards generated by the reward subsystem using reinforcement learning techniques.