Reinforcement Learning Policy Optimization via Objective Function Mapping
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Solution Overview
Problem
Reinforcement learning methods face challenges in efficiently determining optimal policies without requiring closed-form solutions, especially when dealing with unknown reward functions and state distributions, which limits their ability to maximize expected rewards in contextual bandit problems.
Innovation Solution
The method employs an objective function that maps the difference between expected reward estimates and the distance between policies, allowing for iterative updates that maximize a lower bound on the expected reward, using techniques like gradient ascent and incorporating Kullback-Leibler divergence to account for uncertainty, thereby optimizing policy parameters without needing closed-form solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If closed-form solutions are used for Relative Entropy Policy Search, then policy optimization is achieved, but computational feasibility is lost when dealing with unknown reward functions and state distributions
Solution Approach 1:
The patent introduces an intermediary objective function that serves as a proxy for the intractable Relative Entropy Policy Search problem. This objective function includes a lower bound term that can be computed from training data, acting as a mediator between the desired policy optimization and the computational constraints. The intermediary function enables iterative optimization without requiring closed-form solutions.
Solution Approach 2:
The patent replaces the mechanical system of closed-form mathematical solutions with an iterative optimization process. Instead of relying on analytical solutions that break down with unknown reward functions, the system uses gradient-based optimization on the objective function, substituting the computational mechanism to work effectively with unknown distributions.
2Ease of manufacture
If iterative updates are used to maximize lower bound on expected reward, then computational feasibility is improved, but convergence to optimal policy is not guaranteed
Solution Approach 1:
The patent implements feedback through iterative optimization where the objective function is evaluated and updated at each step. The lower bound on expected reward provides a feedback signal that guides the optimization process, allowing the policy to gradually improve based on computed gradients while maintaining computational feasibility at each iteration.
Solution Approach 2:
The patent accepts partial optimization through iterative updates rather than requiring complete convergence to the optimal policy. By maximizing the lower bound at each iteration, the method achieves sufficient improvement in computational feasibility while the iterative process continues to refine the policy, accepting that exact optimality may not be reached but practical performance is achieved.
3Reliability
If Kullback-Leibler divergence is incorporated to account for uncertainty, then policy robustness is improved, but computational complexity increases
Solution Approach 1:
The patent handles the computational complexity of Kullback-Leibler divergence by changing the parameterization approach. Instead of computing the full divergence directly, the method uses a simplified objective function that incorporates the divergence term in a computationally efficient manner, allowing uncertainty accounting without excessive computational burden.
Data Source
AI summary
A device and method for reinforcement learning. The method includes providing parameters of a policy for reinforcement learning, determining a behavior policy depending on the policy, sampling a training data set with the behavior polic, and determining an update for the parameters with an objective function, wherein the objective function maps a difference between an estimate for an expected reward when following the policy and an estimate for a distance between the policy and the behavior policy, that depends on the policy and on the behavior policy, to the update. Or, the method includes providing distribution for parameters of a policy for reinforcement learning, determining a behavior policy depending on the policy, sampling a training data set with the behavior policy, and determining an update for the distribution with another objective function.