Add to ORKGWe present a new connectionist planning method [TML90]. By interaction with an unknown environment, a world model is progressively constructed using gradient descent. For deriving optimal actions with respect to future reinforcement, planning is applied in two steps: an experience network proposes a plan which is subsequently optimized by gradient descent with a chain of world models, so that an optimal reinforcement maybe obtained when it is actually run. The appropriateness of this method is demonstrated by a robotics application and a pole balancing task
In this article we propose an extension for a path planning method based on the LPN-algorithm to hav...
We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, ...
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a k...
Search based planners such as A* and Dijkstra\u27s algorithm are proven methods for guiding today\u2...
In robotics, path planning refers to finding a short. collision-free path from an initial robot conf...
A robot wanders around an unfamiliar environment, performing actions and observing their perceptual ...
Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a...
used robotic motion planning methods that sample robot config-urations (nodes) and connect them to f...
Reinforcement learning algorithms without an internal world model often suffer from overly long time...
By beginning with simple reactive behaviors and gradually building up to more memory-dependent behav...
The Problem: We want to build a robust learning and planning system for robots, in which: A huge mo...
Deep reinforcement learning methods are capable of learning complex heuristics starting with no prio...
Abstract. Reinforcement learning (RL) involves sequential decision making in uncertain environments....
The artificial potential field approach is an efficient path planning method. However, to deal with ...
Abstract. Learning and behaviour of mobile robots faces limitations. In reinforcement learning, for ...
In this article we propose an extension for a path planning method based on the LPN-algorithm to hav...
We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, ...
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a k...
Search based planners such as A* and Dijkstra\u27s algorithm are proven methods for guiding today\u2...
In robotics, path planning refers to finding a short. collision-free path from an initial robot conf...
A robot wanders around an unfamiliar environment, performing actions and observing their perceptual ...
Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a...
used robotic motion planning methods that sample robot config-urations (nodes) and connect them to f...
Reinforcement learning algorithms without an internal world model often suffer from overly long time...
By beginning with simple reactive behaviors and gradually building up to more memory-dependent behav...
The Problem: We want to build a robust learning and planning system for robots, in which: A huge mo...
Deep reinforcement learning methods are capable of learning complex heuristics starting with no prio...
Abstract. Reinforcement learning (RL) involves sequential decision making in uncertain environments....
The artificial potential field approach is an efficient path planning method. However, to deal with ...
Abstract. Learning and behaviour of mobile robots faces limitations. In reinforcement learning, for ...
In this article we propose an extension for a path planning method based on the LPN-algorithm to hav...
We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, ...
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a k...