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- Kartasev, Mart, et al.
(författare)
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Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning
- 2023
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Ingår i: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1572-1579
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Konferensbidrag (refereegranskat)abstract
- In this paper we show how to improve the performance of backward chained behavior trees (BTs) that include policies trained with reinforcement learning (RL). BTs represent a hierarchical and modular way of combining control policies into higher level control policies. Backward chaining is a design principle for the construction of BTs that combines reactivity with goal directed actions in a structured way. The backward chained structure has also enabled convergence proofs for BTs, identifying a set of local conditions to be satisfied for the convergence of all trajectories to a set of desired goal states. The key idea of this paper is to improve performance of backward chained BTs by using the conditions identified in a theoretical convergence proof to configure the RL problems for individual controllers. Specifically, previous analysis identified so-called active constraint conditions (ACCs), that should not be violated in order to avoid having to return to work on previously achieved subgoals. We propose a way to set up the RL problems, such that they do not only achieve each immediate subgoal, but also avoid violating the identified ACCs. The resulting performance improvement depends on how often ACC violations occurred before the change, and how much effort, in terms of execution time, was needed to re-achieve them. The proposed approach is illustrated in a dynamic simulation environment.
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| 2. |
- Kartasev, Mart, et al.
(författare)
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Improving the Performance of Learned Controllers in Behavior Trees Using Value Function Estimates at Switching Boundaries
- 2024
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 9:5, s. 4647-4654
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Tidskriftsartikel (refereegranskat)abstract
- Behavior trees offer a modular approach to developing an overall controller from a set of sub-controllers that solve different sub-problems. These sub-controllers can be created using various methods, such as classical model-based control or reinforcement learning (RL). To achieve the overall goal, each sub-controller must satisfy the preconditions of the next sub-controller. Although every sub-controller may be locally optimal in achieving the preconditions of the next one, given some performance metric like completion time, the overall controller may still not be optimal with respect to the same performance metric. In this paper, we demonstrate how the performance of the overall controller can be improved if we use approximations of value functions to inform the design of a sub-controller of the needs of the next controller. We also show how, under certain assumptions, this leads to a globally optimal controller when the process is executed on all sub-controllers. Finally, this result also holds when some of the sub-controllers are already given. This means that if we are constrained to use some existing sub-controllers, the overall controller will be globally optimal, given this constraint.
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