Reinforcement Learning Control with ASP Planning Horizons
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Solution Overview
Problem
Reinforcement learning methods for training control strategies in robots are slow to converge to an optimal control policy, especially in scenarios where rewards are difficult to find, and existing methods require significant time and resources.
Innovation Solution
Implementing a planning component using answer set programming (ASP) to model the relationship between actions and successor states, allowing for multiple reinforcement learning training passes with a specified planning horizon, which speeds up the learning process by utilizing prior knowledge and optimizing reward sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If simple trial and error-based reinforcement learning methods are used, then the agent can learn from experience without prior knowledge, but the convergence to an optimal control policy is very slow
Solution Approach 1:
The patent applies preliminary action by pre-computing action sequences using answer set programming before the reinforcement learning agent needs to make decisions. The ASP solver generates possible action sequences and their outcomes in advance, allowing the agent to start with a head start rather than learning from scratch through trial and error. This resolves the contradiction by providing prior knowledge (pre-computed sequences) that accelerates convergence while maintaining the agent's ability to learn and adapt.
Solution Approach 2:
The patent introduces an intermediary component - the answer set programming solver - that acts as a bridge between prior knowledge and the reinforcement learning agent. The ASP solver processes the environment model and generates structured action sequences that the RL agent can then use as guidance. This intermediary enables the agent to benefit from pre-computed information without requiring full prior knowledge of the optimal policy, thus speeding up convergence while preserving adaptability.
2Productivity
If a planning component with a small planning horizon is used, then the training speed is significantly increased, but the planning component cannot find the end state directly
Solution Approach 1:
The patent applies segmentation by dividing the overall planning task into smaller segments corresponding to the planning horizon. Instead of attempting to plan the entire sequence from start to end state in one step, the ASP solver breaks down the problem into manageable segments of limited length. Each segment represents a portion of the path toward the end state, and multiple segments are concatenated to form the complete solution. This segmentation enables fast training with small horizons while ultimately achieving the end state through incremental progress.
Solution Approach 2:
The patent employs partial action by having the planning component perform incomplete planning - generating action sequences that reach intermediate states rather than requiring complete plans to the end state. The RL agent then completes the remaining portion of the task from these intermediate points. This partial planning approach maintains the speed benefits of small planning horizons while still making progress toward the end state through multiple iterations of partial plans combined with learning.
3Loss of time
If prior knowledge about the control scenario is used to speed up learning, then the learning process is accelerated, but the model may not reflect all details of the environment
Solution Approach 1:
The patent applies copying by creating a simplified copy of the environment in the form of an answer set programming model. This ASP model captures the essential structure and dynamics of the environment without requiring complete accuracy. The model is a representation or copy that enables fast computation and planning, while the reinforcement learning agent learns the discrepancies between the simplified model and the real environment through interaction. This resolves the contradiction by allowing fast learning with an imperfect model copy.
Solution Approach 2:
The patent uses parameter changes by adjusting the level of detail and accuracy parameters of the environment model. The ASP model uses simplified parameters and assumptions that enable efficient computation, rather than attempting to model every detail of the real environment. The reinforcement learning agent then learns to compensate for the simplifications by adapting its policy based on actual environmental feedback. This parameter adjustment strategy enables fast learning with reduced model complexity while maintaining eventual accuracy.
Data Source
AI summary
A method for training a control strategy with the aid of reinforcement learning. The method includes carrying out passes, in each pass, an action that is to be carried out being selected for each state of a sequence of states of an agent, for at least some of the states the particular action being selected by specifying a planning horizon that predefines a number of states, ascertaining multiple sequences of states, reachable from the particular state, using the predefined number of states, by applying an answer set programming solver to an answer set programming program which models the relationship between actions and the successor states that are reached by the actions, selecting the sequence that delivers the maximum return, and selecting an action as the action for the particular state via which the first state of the selected sequence may be reached, starting from the particular state.