Takashi Kuremoto, Takaomi Hirata, Masanao Obayashi, Shingo Mabu and Kunikazu Kobayashi (April 3rd 2019). Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting, Time Series Analysis - Data, Methods, and Applications, Chun-Kit Ngan, IntechOpen, DOI: 10.5772/intechopen.85457. 

Masakazu Sato, Kaori Koga, Tomoyuki Fujii and Yutaka Osuga (June 27th 2018). Can Reinforcement Learning Be Applied to Surgery?, Artificial Intelligence - Emerging Trends and Applications, Marco Antonio Aceves-Fernandez, IntechOpen, DOI: 10.5772/intechopen.76146.

Lihong Xu, Qingsong Hu, Haigen Hu and Erik Goodman (February 1st 2010). Conflicting Multi-Objective Compatible Optimization Control, New Achievements in Evolutionary Computation, Peter Korosec, IntechOpen, DOI: 10.5772/8050.

New Zealand

Introducing DCRAC, a deep reinforcement learning framework for solving multi-objective partially-observable problems. DCRAC follows a conditioned actor-critic approach in
learning the optimal policy.

AAMAS 2020

DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments

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