Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

Wp-dyna: Planning and Reinforcement Learning in Well-plannable Environments

Reinforcement learning (RL) involves sequential decision making in uncertain environments. The aim of the decision-making agent is to maximize the benefit of acting in its environment over an extended period of time. Finding an optimal policy in RL may be very slow. To speed up learning, one often used solution is the integration of planning, for example, Sutton’s Dyna algorithm, or various oth...

Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems

In a Role-Playing Game, finding optimal trajectories is one of the most important tasks. In fact, the strategy decision system becomes a key component of a game engine. Determining the way in which decisions are taken (online, batch or simulated) and the consumed resources in decision making (e.g. execution time, memory) will influence, in mayor degree, the game performance. When classical sear...

The Z Method for Fast Path Planning in Dynamic Environments

We present a method to plan collision free paths for robots with any number of degrees of freedom in dynamic environments The method proved to be very e cient as it ommits a complete representation of the high di mensional search space Its complexity is linear in the number of degrees of freedom A preprocessing of the geometry data of the robot or the environment is not re quired With the time ...

Path Planning in Dynamic Environments

The motion planning problem for mobile robots is typically formulated as follows: given a robot and a description of an environment, plan a path of the robot between two specified locations, which is collision-free and satisfies certain optimization criteria. Traditionally there are two approaches to the problem: Off-line planning, which assumes perfectly known and stable environment, and on-li...

Path Planning in Dynamic Environments