Ensemble Q Learning for Sample-Efficient Model-Based RL
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Solution Overview
Problem
Reinforcement learning systems struggle to learn effective policies in complex real-world environments due to error-prone predictions from environment dynamics models, leading to bias and inefficiency, particularly in mechanical agents like robots, where sample collection is costly and time-intensive.
Innovation Solution
Employing ensembles of Q networks, transition models, and reward models to generate target Q values by interpolating between trajectories, accounting for model uncertainty and bias, reducing the need for actual environment samples and computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If model-based reinforcement learning approaches use a dynamics model to predict future states, then the complexity and time required to learn the policy is greatly reduced, but predictions made by the dynamics model become error-prone and introduce bias into the learning process in complex real-world environments
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate trajectories using the dynamics model before selecting the final action. Multiple Q networks evaluate these pre-generated trajectories, and the system interpolates between them to account for model uncertainty. This preliminary exploration of multiple possible futures allows the system to reduce reliance on single-model predictions while maintaining computational efficiency.
Solution Approach 2:
The system introduces an intermediary mechanism - the ensemble of Q networks that evaluate and interpolate between multiple trajectories generated by the dynamics model. This intermediary layer acts as a buffer that corrects for the bias and errors in the dynamics model predictions by considering multiple possible outcomes and weighting them appropriately, thus resolving the contradiction between using model predictions and avoiding their errors.
2Quantity of substance
If ensembles of Q networks, transition models, and reward models are employed to generate target Q values by interpolating between trajectories, then sample complexity is reduced, but computational intensity increases
Solution Approach 1:
The system creates computational copies of the environment dynamics through multiple transition models and generates multiple candidate trajectories from a single actual environment sample. These synthetic copies allow the system to extract more information from fewer actual environment interactions, reducing sample complexity while the computational cost is managed through efficient interpolation techniques rather than requiring proportionally more actual samples.
3Measurement precision
If multiple different trajectories are generated from a single transition and target Q values are interpolated between them, then dynamics model uncertainty and Q network bias are accounted for, but the computational overhead of maintaining ensembles increases
Solution Approach 1:
The system performs partial action by generating multiple trajectories and using interpolation to obtain the necessary information for training, rather than requiring exhaustive exploration of all possible trajectories or using a single overly complex model. This partial approach - generating just enough trajectories and using efficient interpolation - achieves the goal of accounting for uncertainty and bias without the excessive computational cost of maintaining equally complex alternative systems.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sample-efficient reinforcement learning. One of the methods includes maintaining an ensemble of Q networks, an ensemble of transition models, and an ensemble of reward models; obtaining a transition; generating, using the ensemble of transition models, M trajectories; for each time step in each of the trajectories: generating, using the ensemble of reward models, N rewards for the time step, generating, using the ensemble of Q networks, L Q values for the time step, and determining, from the rewards, the Q values, and the training reward, L*N candidate target Q values for the trajectory and for the time step; for each of the time steps, combining the candidate target Q values; determining a final target Q value; and training at least one of the Q networks in the ensemble using the final target Q value.