Robot Policy Learning With On-Policy Dynamics Error Correction
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Solution Overview
Problem
In reinforcement learning, the learned model is inevitably an approximation of the real world, leading to slow convergence and sub-optimal solutions due to model errors, especially when simulating long horizons, which compounds errors and makes tuning hyperparameters challenging.
Innovation Solution
The proposed method learns a transition dynamics model that combines a global model and an on-policy correction term, where the correction term is state-dependent, reducing on-policy model errors and improving stability and data-efficiency by focusing on relevant parts of the global model for policy optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a learned model is used to approximate real world dynamics, then policy optimization can be performed without direct environment interaction, but model errors compound over long horizons leading to slow convergence and sub-optimal solutions
Solution Approach 1:
The model is segmented into two distinct components: a global model that captures general dynamics across all states and actions, and a correction model that specifically addresses on-policy errors. This segmentation allows each component to specialize - the global model provides broad coverage while the correction model focuses computational resources on reducing errors in the specific policy-relevant regions, thereby maintaining reliability over long horizons without sacrificing optimization efficiency.
Solution Approach 2:
The correction model applies local quality improvement by focusing computational effort on states and actions that are actually visited by the current policy, rather than uniformly improving accuracy across all possible states. This is achieved by weighting correction losses based on policy visitation probabilities, ensuring that model accuracy is enhanced precisely where it matters most for policy optimization while avoiding waste on rarely-visited states.
2Adaptability or versatility
If model training focuses on global dynamics approximation, then coverage across all states is improved, but on-policy errors remain significant slowing down policy convergence
Solution Approach 1:
The model is divided into a global component that maintains broad state coverage and a correction component that specifically targets on-policy accuracy. The global model ensures adaptability by learning from diverse transitions across the state space, while the correction model reduces convergence time by focusing computational resources on minimizing errors in policy-relevant regions through weighted loss functions based on visitation probabilities.
Solution Approach 2:
The final dynamics model merges the global model and correction model through additive combination: p(s'|s,a) = p_global(s'|s,a) + p_correction(s'|s,a). This merging allows the system to benefit from both broad coverage (from the global model trained on diverse data) and high on-policy accuracy (from the correction model focused on policy-relevant transitions), resolving the contradiction between adaptability and convergence speed.
3Reliability
If simulation horizon is reduced to minimize error compounding, then model error impact is reduced, but the fundamental problem of model errors from rollouts remains and hyperparameter tuning becomes challenging
Solution Approach 1:
The correction model is trained using feedback from the actual differences between global model predictions and observed transitions, weighted by policy visitation probabilities. This feedback mechanism automatically adapts the correction strength based on actual performance, eliminating the need for manual hyperparameter tuning of simulation horizons. The system self-regulates by focusing corrections where they are most needed, reducing error compounding without requiring complex hyperparameter management.
4Adaptability or versatility
If data collection focuses on all possible states, then global model coverage is improved, but data-efficiency for policy optimization decreases due to irrelevant states
Solution Approach 1:
The correction model applies local quality enhancement by concentrating data collection and correction efforts on states and actions that are actually visited by the current policy. Transitions are weighted by their visitation probabilities under the policy, ensuring that data efficiency is maximized for policy-relevant regions. This allows the system to maintain good global coverage through the global model while achieving high data efficiency in the correction model by ignoring or down-weighting rarely-visited states.
Data Source
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AI summary
A computer-implemented method for for learning a policy, comprising the steps of: Recording at least an episode of interactions of the agent with its environment following policy and adding the recorded episode to a set of training data. Optimizing a transition dynamics model based on the training data such that the transition dynamics model predicts the next states of the environment depending on the states and actions contained in the training data. Optimizing policy parameters based on the training data and the transition dynamics model by optimizing a reward. The method is characterized in that the transition dynamics model comprises a first model characterizing the global model and a second model characterizing a correction model, which is configured to correct outputs of the first model.