Offline Reinforcement Learning With Robust Reward Model Aggregation
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Solution Overview
Problem
Reinforcement learning algorithms often require clear reward signals, which are subjective and imprecise in real-world scenarios, leading to unreliable agent behavior, and online reinforcement methods may not be feasible in environments where interaction is expensive, dangerous, or unavailable.
Innovation Solution
Implement a system that trains multiple reinforcement learning models on an offline dataset using diverse reward models and a robustness operator to generate a robust policy, leveraging offline datasets and imprecise reward signals to produce reliable behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning algorithms use traditional reward signals, then the learning process can proceed, but the policies produced are suboptimal due to unreliable and imprecise reward signals
Solution Approach 1:
The patent combines multiple diverse reward models into an ensemble that produces a single robust reward signal. By merging the predictions of multiple models through a robustness operator (such as tau-percentile aggregation), the system overcomes the imprecision of individual reward signals and generates more reliable policies for offline reinforcement learning.
Solution Approach 2:
The patent introduces a robustness operator as an intermediary component between the multiple reward models and the reinforcement learning algorithm. This operator aggregates the outputs of diverse reward models and filters out unreliable signals, providing a refined reward estimate that improves policy reliability without requiring direct interaction with the environment.
2Reliability
If interaction with the environment is used for data collection, then the reinforcement learning algorithm can learn from real feedback, but the process becomes expensive or dangerous in certain environments
Solution Approach 1:
The patent performs preliminary actions by training multiple diverse reward models offline on available datasets before the actual reinforcement learning process. This preliminary training of reward models allows the system to evaluate policies without requiring extensive environment interaction, reducing the cost and risk of data collection while maintaining policy robustness.
Solution Approach 2:
The patent creates copies of reward evaluation through multiple diverse reward models instead of relying on single environment interactions. These model copies provide alternative perspectives on reward signals, enabling the system to learn robust policies from offline data without repeatedly interacting with the expensive or dangerous environment.
3Reliability
If a single reward model is used, then the system complexity is low, but the policy performance deteriorates due to unreliable reward signals
Solution Approach 1:
The patent changes the parameters of the reward evaluation system by using multiple diverse models with different architectures, hyperparameters, or training configurations. By varying these parameters across multiple models and aggregating their outputs through a robustness operator, the system achieves more reliable reward signals while managing complexity through diverse but coordinated model ensembles.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for reinforcement learning is provided. The present invention may include training, using an offline dataset, a plurality of diverse reward models, and creating a policy based on an output of the reward models and a robustness operator of the reward models.