Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Gattami, Ather"'
Autor:
Gattami, Ather
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximat
Externí odkaz:
http://arxiv.org/abs/2402.19212
We consider a decentralized multiplayer game, played over $T$ rounds, with a leader-follower hierarchy described by a directed acyclic graph. For each round, the graph structure dictates the order of the players and how players observe the actions of
Externí odkaz:
http://arxiv.org/abs/2301.11802
Autor:
Kjellqvist, Olle, Gattami, Ather
In this paper, we treat linear quadratic team decision problems, where a team of agents minimizes a convex quadratic cost function over $T$ time steps subject to possibly distinct linear measurements of the state of nature. We assume that the state o
Externí odkaz:
http://arxiv.org/abs/2212.11567
Autor:
Terelius, Håkan, Shi, Guodong, Dowling, Jim, Payberah, Amir, Gattami, Ather, Johansson, Karl Henrik
In this paper, we investigate the topology convergence problem for the gossip-based Gradient overlay network. In an overlay network where each node has a local utility value, a Gradient overlay network is characterized by the properties that each nod
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-46515
In the optimization of dynamical systems, the variables typically have constraints. Such problems can be modeled as a constrained Markov Decision Process (CMDP). This paper considers a model-free approach to the problem, where the transition probabil
Externí odkaz:
http://arxiv.org/abs/2006.05961
In the optimization of dynamic systems, the variables typically have constraints. Such problems can be modeled as a Constrained Markov Decision Process (CMDP). This paper considers the peak Constrained Markov Decision Process (PCMDP), where the agent
Externí odkaz:
http://arxiv.org/abs/2003.05555
We present a representation learning framework for financial time series forecasting. One challenge of using deep learning models for finance forecasting is the shortage of available training data when using small datasets. Direct trend classificatio
Externí odkaz:
http://arxiv.org/abs/2002.07638
In this paper, we consider the problem of optimization and learning for constrained and multi-objective Markov decision processes, for both discounted rewards and expected average rewards. We formulate the problems as zero-sum games where one player
Externí odkaz:
http://arxiv.org/abs/1901.08978
Autor:
Gattami, Ather
In this paper, we consider reinforcement learning of Markov Decision Processes (MDP) with peak constraints, where an agent chooses a policy to optimize an objective and at the same time satisfy additional constraints. The agent has to take actions ba
Externí odkaz:
http://arxiv.org/abs/1901.07839
Autor:
Bernhardsson, Bo, Gattami, Ather
The energy-optimal scheme is found for communicating one bit over a memoryless Gaussian channel with an ideal feedback channel. It is assumed that the channel is allowed to be used at most N times before decoding. The optimal coding/decoding strategy
Externí odkaz:
http://arxiv.org/abs/1605.04579