AI Planning Annotation in RL for Sequential Decision-Making
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Solution Overview
Problem
Current AI planning and reinforcement learning (RL) methods have different approaches to solving sequential decision-making problems, with AI planning being efficient for large-scale state transition systems but requiring operator models, and RL learning policies for high reward states, yet being sample inefficient in sparse reward scenarios.
Innovation Solution
The integration of AI planners and RL agents through AI planning annotation in RL (PaRL) generates a PaRL task by mapping Markov decision process states to AI planning states, using an options framework to select and rank options for training, and guiding sampling processes to improve RL agent performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI planning is used to solve sequential decision-making problems, then efficiency in large-scale state transition systems is improved, but requirement for operator models increases system complexity
Solution Approach 1:
The patent introduces an intermediary mapping layer that translates MDP states to AI planning states, enabling AI planning to operate on transformed state representations without requiring the RL system to directly handle complex operator models, thus resolving the contradiction between efficiency and complexity
Solution Approach 2:
The patent segments the problem-solving process into distinct components: MDP state representation, state space mapping to planning states, AI planning task execution, and option generation. This segmentation allows each component to be optimized independently, improving overall efficiency while managing complexity through modular architecture
2Reliability
If RL learns policies for high reward states, then ability to solve sequential decision-making problems is improved, but sample efficiency deteriorates in sparse reward scenarios
Solution Approach 1:
The patent performs preliminary action by using AI planning to generate candidate option sequences before RL training. These pre-generated plans serve as informed initial policies that guide the RL agent, reducing the number of samples needed to learn effective policies in sparse reward environments
Solution Approach 2:
The system uses itself iteratively: AI planning generates initial options, RL trains on these options to improve policy, then the improved policy generates better plans through planning, creating a self-reinforcing cycle that progressively improves sample efficiency while maintaining problem-solving capability
3Measurement precision
If AI planning and RL are integrated through state space mapping, then accuracy in solving sequential decision-making problems is improved, but processing power and storage usage increase
Solution Approach 1:
The patent extracts only the essential state features needed for planning by mapping MDP states to planning states through a carefully designed mapping function. This extraction process selects and transforms only relevant state information, maintaining accuracy while reducing the computational burden of processing complete state representations
Solution Approach 2:
The patent changes the parameter representation of states by transforming MDP states into planning state representations with different dimensionalities and structures. This parameter transformation allows the system to maintain precise state representation for accurate planning while using compressed or abstracted state features that require less processing power and storage
4Loss of time
If options framework is used to select and rank options for training, then sample efficiency of RL algorithms is improved, but device complexity increases
Solution Approach 1:
The patent introduces an options framework as an intermediary layer between AI planning and RL training. This framework ranks and selects candidate options based on planning-generated plans, providing a structured mechanism that improves sample efficiency by focusing training on high-value options while managing complexity through hierarchical organization of the training process
Data Source
AI summary
A computer-implemented method of integrating an Artificial Intelligence (AI) planner and a reinforcement learning (RL) agent through AI planning annotation in RL (PaRL) includes identifying an RL problem. A description received of a Markov decision process (MDP) having a plurality of states in an RL environment is used to generate an RL task to solve the RL problem. An AI planning model described in a planning language is received, and mapping state spaces from the MDP states in the RL environment to AI planning states of the AI planning model is performed. The RL task is generated with an AI planning task from the mapping to generate a PaRL task.