Retrieval-Augmented Reinforcement Learning Agent
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Solution Overview
Problem
Existing reinforcement learning systems face inefficiencies in action selection and training due to reliance on pre-programmed logic and computationally expensive data selection methods, which can lead to slow training and high resource requirements, especially for complex tasks.
Innovation Solution
The proposed method introduces a retrieval-augmented action selection process that uses a transition attention mechanism to dynamically access relevant contextual information from past trajectories, updating the policy neural network hidden state and improving data utilization during training, thereby enhancing action selection and reducing training time and resource needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-programmed logic is used for action selection, then the system is easier to implement, but the training speed and adaptability deteriorate
Solution Approach 1:
The patent replaces pre-programmed logic with a neural network-based policy that learns from data. The neural network processes observations and selects actions based on learned patterns rather than explicit programming, enabling faster training and better adaptability while maintaining implementation feasibility through standard deep learning frameworks.
2Loss of information
If computationally expensive data selection methods are used, then the data utilization is improved, but the training time and resource requirements increase
Solution Approach 1:
The patent performs preliminary encoding of trajectories into a compressed representation format before training. This pre-processing step organizes and prepares the data efficiently, allowing the neural network to access and process information faster during training without requiring computationally expensive real-time data selection methods.
Solution Approach 2:
The patent extracts key information from raw trajectories by encoding them into condensed representations that capture essential state-action-reward patterns. This extraction process reduces data complexity and computational requirements while preserving the most important learning signals, thereby reducing training time and resource needs.
3Reliability
If more training data is used, then the model performance is improved, but the computational resources and training time increase
Solution Approach 1:
The patent changes the representation parameters of training data by encoding trajectories into compressed formats with reduced dimensionality. This parameter transformation maintains the essential information needed for learning while significantly reducing the computational burden, allowing high model performance with fewer computational resources and shorter training times.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a reinforcement learning agent in an environment to perform a task using a retrieval-augmented action selection process. One of the methods includes receiving a current observation characterizing a current state of the environment; processing an encoder network input comprising the current observation to determine a policy neural network hidden state that corresponds to the current observation; maintaining a plurality of trajectories generated as a result of the reinforcement learning agent interacting with the environment; selecting one or more trajectories from the plurality of trajectories; updating the policy neural network hidden state using update data determined from the one or more selected trajectories; and processing the updated hidden state using a policy neural network to generate a policy output that specifies an action to be performed by the agent in response to the current observation.