Geometric Policy Composition for Reinforcement Learning
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Solution Overview
Problem
Existing reinforcement learning systems face challenges in effectively selecting actions for complex tasks, such as continuous robotic control, due to limitations in exploring diverse action selection policies and efficiently evaluating new policies.
Innovation Solution
The system employs a generalized policy improvement technique that utilizes a diverse pool of action selection policies, including base and composite policies, and leverages generative neural networks to model future state-visitation distributions, enabling the evaluation of new composite policies without additional computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a diverse pool of action selection policies is used to control the agent, then the ability to explore diverse action spaces and handle complex tasks is improved, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the policy space into multiple independent base policies, each representing a distinct action selection strategy. These base policies are then combined to form composite policies through geometric mixing, allowing the system to explore diverse action spaces by composing and recombining simple policy units rather than managing a single complex policy network.
Solution Approach 2:
The patent creates a universal framework where the same set of base policies can be combined in multiple ways to form different composite policies. The geometric policy composition mechanism allows a single set of base policies to serve multiple functions by varying the mixing probabilities and composition weights, thereby achieving diverse behavior from a unified policy foundation.
2Measurement precision
If generative neural networks are used to model future state-visitation distributions for policy evaluation, then the accuracy of reward estimation is improved, but the computational overhead increases
Solution Approach 1:
The patent pre-trains generative neural networks to model future state-visitation distributions during an offline phase. These pre-trained models can then be used for policy evaluation during the online phase without requiring additional training computational resources. The preliminary action of pre-training enables accurate reward estimation during execution while minimizing real-time computational overhead.
Solution Approach 2:
The patent uses generative neural networks to create simulated copies of future state-visitation distributions rather than actually executing the policies to observe real outcomes. This copying approach allows the system to evaluate policy rewards by sampling from the generated distributions, which are computationally cheaper than actual policy execution while providing sufficiently accurate reward estimates for effective policy selection.
3Adaptability or versatility
If composite policies switching between multiple base policies are used, then the flexibility and adaptability of the agent is improved, but the difficulty of evaluating and selecting optimal policies increases
Solution Approach 1:
The patent implements a feedback mechanism where the evaluation of composite policies is guided by the performance of their constituent base policies. The generative neural networks provide feedback about expected future states and rewards for each base policy, which then informs the evaluation of composite policies formed by combining these base policies. This feedback loop simplifies the evaluation process by breaking down the complex composite policy assessment into manageable base policy evaluations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling a reinforcement learning agent in an environment. One of the methods may include maintaining data specifying a base policy set comprising a plurality of base policies for controlling the agent; receiving a current observation characterizing a current state of the environment; generating, for each of the plurality of base policies, one or more predicted future observations characterizing respective future states of the environment that are subsequent to the current state of the environment; using the predicted future observations generated for the plurality of base policies to determine a respective estimated value for each composite policy in a composite policy set with respect to the current state of the environment; and selecting an action using the respective estimated values for the composite policies.