Synthetic Return Neural Network for Long-Term Credit Assignment
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Solution Overview
Problem
Conventional reinforcement learning systems face difficulties in learning tasks that involve long-term credit assignment, as they struggle to evaluate the utility of actions with consequences that are not experienced until a long time delay, leading to slow learning or failure in tasks with delayed rewards.
Innovation Solution
The system employs a synthetic return neural network system and episodic memory to learn associations between past and current states, generating a synthetic reward that can be used to train the agent to take actions benefiting from future rewards, even when those rewards are delayed, by decomposing rewards into components explainable by different past states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reinforcement learning systems are used to evaluate actions with delayed consequences, then the system can operate with simple reward structures, but the learning efficiency deteriorates when rewards are delayed over long time scales
Solution Approach 1:
The system performs preliminary action by storing latent representations of past states in episodic memory before the reward is actually received. This allows the system to have state information ready and available when the delayed reward arrives, enabling immediate credit assignment without waiting for the reward signal.
Solution Approach 2:
The synthetic return neural network acts as an intermediary between the delayed reward signal and the policy gradient computation. It decomposes the delayed reward into synthetic returns that can be attributed to specific past states, bridging the temporal gap between action and consequence.
2Adaptability or versatility
If the system stores and processes latent representations from episodic memory to enable long-term credit assignment, then the capability to learn from delayed rewards improves, but the computational complexity increases
Solution Approach 1:
The system segments the computational task by separating the storage of latent representations (episodic memory) from the processing of rewards (synthetic return network). This segmentation allows each component to be optimized independently and reduces the overall computational burden compared to processing all state-reward pairs simultaneously.
Solution Approach 2:
The system extracts only the necessary latent representations from past states and stores them in episodic memory, rather than storing complete state information. This extraction reduces memory requirements and computational complexity while retaining the essential information needed for credit assignment.
3Measurement precision
If the synthetic return neural network decomposes rewards into components explainable by different past states, then the precision of credit assignment improves, but the training complexity increases
Solution Approach 1:
The synthetic return neural network uses feedback from actual rewards received at later time steps to train the associations between latent representations and reward contributions. This feedback mechanism enables the system to progressively improve its credit assignment precision by learning from the actual outcomes of past actions.
Data Source
AI summary
A computer-implemented reinforcement learning neural network system that learns a model of rewards in order to relate actions by an agent in an environment to their long-term consequences. The model learns to decompose the rewards into components explainable by different past states. That is, the model learns to associate when being in a particular state of the environment is predictive of a reward in a later state, even when the later state, and reward, is only achieved after a very long time delay.