networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph data mining tasks...
Since the value function is shared among agents, sub-optimal policies from one agent can have a detrimental impact on the policy learning of other agents, causing catastrophic miscoordination 以下我认为讲的是对于MARL的tricks 对于经典的RL问题的理解也有帮助 Parameter Sharing implement centralization, whe...
Our survey provides the necessary background for operations research and machine learning communities and showcases the works that are moving the field forward. We juxtapose recently proposed RL methods, laying out the timeline of the improvements for each problem, as well as we make a comparison ...
This is a dominant reason for the wide attention this model has received. 240 Reinforcement Learning: A Survey Another optimality criterion is the average-reward model, in which the agent is supposed to take actions that optimize its long-run average reward: h 1X lim E ( h rt ) : h!1...
再比如Learning to plan,就是说planning也不是制定好的方式,比如MCTS之类的,而是像policy一样去学出来的(The idea is to optimize our planner over a sequence of tasks to eventually obtain a better planning algorithm, which is a form of meta-learning)。文中举的例子是MCTSNets,Imagination-augmented agent...
我可能需要再学习一下。 参考 ^Alfonso Sorrentino, Lecture notes on Mather theory for Lagrangian systems ^Evans,A survey of PDE methds in weak KAM theory ^Powell, From reinforcement learning to optimal control_a unified framework for sequential decisions...
Reinforcement learning means learning a policy-a mapping of observations into actions-based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. We present an application of a gradient asce...
DEEP learningINTERNETWORKINGThis paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-t...
The red arrows highlight primary steps, involving experience sampling from both the model and the real environment for policy and model learning. The model framework incorporates embedding layers for state and action feature extraction, followed by merging based on graph network topology. b, ...
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of recent trends of deep reinforcement learning in recommender systems. We start by motivating ...