Agent Control via Shared Latent RL Across State Representations
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Solution Overview
Problem
Reinforcement Learning (RL) requires a substantial amount of training data and interactions with the environment, especially for complex tasks, leading to high costs and inefficiencies, particularly when training in the real world is necessary, and simulations may result in poor outcomes.
Innovation Solution
A method that collects training data for multiple representations of an agent's states, using shared state and action encoders and decoders to map between latent and actual states and actions, and a shared transition and Q-function model, allowing knowledge transfer across representations, such as from simulations to real-world scenarios, to reduce training effort and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is used to train an agent for complex tasks, then the agent can learn desired behavior, but a considerable quantity of training data and interactions with the environment are required, leading to high costs
Solution Approach 1:
The patent creates virtual copies of the real-world environment through simulation. A simulation model is trained to replicate the dynamics and characteristics of the real environment, allowing the agent to undergo extensive training in the virtual copy without incurring the high costs of real-world interactions. This copying approach enables the agent to learn desired behaviors while significantly reducing the quantity of real training data required.
Solution Approach 2:
The patent performs preliminary training actions in the simulation environment before deploying the agent to the real world. The simulation is used to pre-train the agent on complex tasks, allowing it to acquire foundational knowledge and skills in advance. This preliminary action in the virtual environment reduces the burden of real-world training data collection while maintaining training effectiveness.
2Quantity of substance
If training is conducted solely in simulations, then training costs are reduced, but poor results are obtained
Solution Approach 1:
The patent merges simulation training with real-world training in a hybrid approach. The system combines the advantages of both virtual and real environments by using simulation for preliminary training and real-world data for fine-tuning and validation. This merging ensures that training costs are reduced through simulation while maintaining result quality through real-world verification and adaptation.
Solution Approach 2:
The patent implements a feedback mechanism where the agent's performance in the real world is used to update and refine the simulation model. Real-world observations and outcomes are fed back into the simulation environment to improve its accuracy and realism. This feedback loop ensures that simulation-based training produces reliable results by continuously aligning the virtual environment with real-world dynamics.
3Measurement precision
If training in the real world is conducted, then high-quality training data is obtained, but high expenditure is required due to hardware and human assistance
Solution Approach 1:
The patent creates a virtual copy of the real-world environment that replicates its characteristics and dynamics. This simulation copy allows the system to obtain high-quality training data in a cost-effective manner by performing computations and data collection in the virtual environment rather than requiring expensive real-world hardware and human assistance for every training iteration.
Solution Approach 2:
The patent performs preliminary training and data collection actions in the simulation environment before deploying to the real world. High-quality training data is pre-collected and pre-processed in the virtual environment, reducing the need for expensive real-world training operations while maintaining data quality through careful simulation design and validation.
Data Source
AI summary
A method for controlling an agent. The method includes collecting training data for multiple representations of states of the agent; for every representation and using the training data, training a state encoder, a state decoder, an action encoder and an action decoder, and a transition model, shared for the representations, for latent states, and a Q function model, shared by the representations, for latent states; receiving a state of the agent in one of the representations for which a control action is to be ascertained; mapping the state to one or more latent state(s) using the state encoder for the one of the representations; determining Q values for the state(s) for a set of actions using the Q function model; selecting the control action having the best Q value from the set of actions as the control action; and controlling the agent according to the selected control action.