Deep reinforcement learning algorithms typically act on the same set of actions. However, this is not sufficient for a wide range of real-world applications where different subsets are available at each step. In this thesis, we consider the problem of interval restrictions as they occur in pathfinding with dynamic obstacles. When actions that lead to collisions are avoided, the continuous action space is split into variable parts. Recent research learns with strong assumptions on the number of intervals, is limited to convex subsets, and the available actions are learned from the observations. Therefore, we propose two approaches that are independent of the state of the environment by extending parameterized reinforcement learning and ConstraintNet to handle an arbitrary number of intervals. We demonstrate their performance in an obstacle avoidance task and compare the methods to penalties, projection, replacement, as well as discrete and continuous masking from the literature. The results suggest that discrete masking of action-values is the only effective method when constraints did not emerge during training. When restrictions are learned, the decision between projection, masking, and our ConstraintNet modification seems to depend on the task at hand. We compare the results with varying complexity and give directions for future work.

翻译：深度强化学习算法通常作用于相同的动作集。然而，在许多实际应用中，每一步可用的动作子集各不相同，这远远不够。本文研究了动态障碍物路径规划中出现的区间限制问题。当避免导致碰撞的动作时，连续动作空间被分割为可变部分。近期研究假设区间数量较强，局限于凸子集，且可用动作从观测中学习。因此，我们提出两种不依赖环境状态的方法，通过扩展参数化强化学习和ConstraintNet来处理任意数量的区间。我们在避障任务中展示了其性能，并与文献中的惩罚、投影、替换以及离散和连续掩码方法进行了比较。结果表明，当约束在训练期间未出现时，动作值的离散掩码是唯一有效的方法。当学习约束时，投影、掩码和我们的ConstraintNet修改之间的选择似乎取决于具体任务。我们比较了不同复杂度下的结果，并指出了未来工作的方向。