Imitation Learning Policy Model Using Fitted Q Iteration
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Solution Overview
Problem
Reinforcement learning systems face challenges in sample efficiency and practicality, especially in environments like self-driving cars, where obtaining realistic trial data is difficult and reward frequencies are insufficient for timely learning, and creating effective reward functions is complex.
Innovation Solution
Combining reinforcement learning with imitation learning using fitted Q iteration, where an expert policy is determined, and a soft Bellman backup with penalty is used to estimate the optimal value, allowing for optimization of the reinforcement learning model with expert demonstrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used in environments like self-driving cars, then the system can learn optimal policies through trial and error, but sample efficiency is poor and learning time is excessive due to difficulty in obtaining realistic trial data
Solution Approach 1:
The patent combines reinforcement learning with imitation learning by integrating expert demonstrations into the RL training process. The expert policy generator creates synthetic training data that mimics real expert behavior, which is then used to pre-train or fine-tune the RL agent. This merging allows the system to leverage both the exploratory power of RL and the efficiency of imitation learning from expert examples.
Solution Approach 2:
The system creates synthetic copies of expert demonstrations through the expert policy generator. These synthetic trajectories replicate real expert behavior in the environment without requiring actual real-world expert data collection. The copied expert policies serve as training data that enables the RL agent to learn efficient behaviors without needing to observe real experts performing tasks.
2Reliability
If reinforcement learning is used in environments like self-driving cars, then the system can optimize actions for long-term reward, but creating effective reward functions is complex and reward frequencies are insufficient for timely learning
Solution Approach 1:
The patent extracts the reward function complexity from the RL training process by using expert demonstrations that inherently encode the desired behavior. Instead of designing complex reward functions to guide learning, the system extracts target behaviors directly from expert policies and uses these as training targets. This shifts the complexity from the reward function to the data collection process.
Solution Approach 2:
The system implements feedback through the expert policy generator that continuously refines synthetic training data based on performance metrics. The expert policy is updated based on observed successes and failures, creating a feedback loop that improves the quality of training data over time. This feedback mechanism allows the system to learn from both successful and failed attempts efficiently.
3Quantity of substance
If traditional reinforcement learning is used, then the system can learn from trial data, but obtaining realistic trial data is difficult and requires numerous mistakes
Solution Approach 1:
The system performs preliminary action by pre-generating synthetic expert demonstrations before the main RL training process. The expert policy generator creates a rich dataset of successful expert behaviors in advance, which serves as high-quality training data. This preliminary data generation eliminates the need for the RL agent to make numerous mistakes during the learning process, as the synthetic data provides direct examples of optimal behavior.
Data Source
AI summary
Methods and systems for learning a policy model include determining an imitation learning expert policy. A policy model neural network is iteratively trained using the determined imitation learning expert policy, including modifying the policy model neural network at iteration to decrease a difference between an output of the policy model neural network and a target signal that is based on the determined imitation learning expert policy.