Self-Organisation of Generic Policies in Reinforcement Learning
نویسندگان
چکیده
We propose the use of an exploratory self-organised policy to initialise the parameters of the function approximation in the reinforcement learning policy based on the value function of the exploratory probe in a low-dimensional task. For a high-dimensional problems we exploit the property of the exploratory behaviour to establish a coordination among the degrees of freedom of a robot without any explicit knowledge of the configuration of the robot or the environment. The approach is illustrated by a learning tasks in a six-legged robot. Results show that the initialisation based on the exploratory value function improve the learning speed in the low-dimensional task and that some correlation towards a higher reward can be acquired in the high-dimensional task.
منابع مشابه
Reinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملAn ISU Dialogue System Exhibiting Reinforcement Learning of Dialogue Policies: Generic Slot-Filling in the TALK In-car System
We demonstrate a multimodal dialogue system using reinforcement learning for in-car scenarios, developed at Edinburgh University and Cambridge University for the TALK project. This prototype is the first “Information State Update” (ISU) dialogue system to exhibit reinforcement learning of dialogue strategies, and also has a fragmentary clarification feature. This paper describes the main compon...
متن کاملDecomposition of Reinforcement Learning for Admission Control of Self-Similar Call Arrival Processes
This paper presents predictive gain scheduling, a technique for simplifying reinforcement learning problems by decomposition. Link admission control of self-similar call traffic is used to demonstrate the technique. The control problem is decomposed into on-line prediction of near-future call arrival rates, and precomputation of policies for Poisson call arrival processes. At decision time, the...
متن کاملSelf-Supervision for Reinforcement Learning
Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquit...
متن کامل