… for multiagent reinforcementlearning. A common feature of these algorithms is a parameterized, structured representation of a policy or value function. This structure is leveraged in an …
… In order to learn a representation for actions that captures this structure, we consider a … A common method for learning a policy parameterized with weights θ is to optimize the …
… We then formulate structure as a particular kind of side … of incorporating structure into the RL learning process and … research while providing a common reference point for understanding …
Deep reinforcementlearning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go …
Y Jin, D Kostadinov, M Bouzid… - Machine Learning for …, 2019 - Springer
… We reveal common system structures and create a meta-model for capacity … structures) from common system structures or learn them from related domains or problems, future learning …
Cooperative multi-agent reinforcementlearning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show that …
… they take advantage of the special structure of reinforcement-learning problems that is not … A very common formulation of cost functions in adaptive control are quadratic penalties on …
Z Ding, Y Huang, H Yuan, H Dong - Deep reinforcement learning …, 2020 - Springer
… -critic structure, which leads to a large number of advanced deep reinforcementlearning algorithms. … of terminology for common contents in reinforcementlearning is provided as follows: …
… learning that gains efficiency by capitalizing on task structure. … experiment we report) employ a common set of methods, with … Our approach aims to learn a structure-dependent bandit …