Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG-RL works in a two-level manner. At the first level, SG-RL uses a geometric path-planning method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG-RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG-RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.Comment: 20 page

Manageable creativity

This article notes a perception in mainstream management theory and practice that creativity has shifted from being disruptive or destructive to 'manageable'. This concept of manageable creativity in business is reflected in a similar rhetoric in cultural policy, especially towards the creative industries. The article argues that the idea of 'manageable creativity' can be traced back to a 'heroic' and a 'structural' model of creativity. It is argued that the 'heroic' model of creativity is being subsumed within a 'structural' model which emphasises the systems and infrastructure around individual creativity rather than focusing on raw talent and pure content. Yet this structured approach carries problems of its own, in particular a tendency to overlook the unpredictability of creative processes, people and products. Ironically, it may be that some confusion in our policies towards creativity is inevitable, reflecting the paradoxes and transitions which characterise the creative process