Add to ORKGIn many decision-making problems, agents aim to balance multiple, possibly conflicting objectives. Existing research in deep reinforcement learning mainly focuses on fully-observable single-objective solutions. In this paper, we propose DCRAC, a deep reinforcement learning framework for solving partially-objective multi-objective problems. DCRAC follows a conditioned actor-critic approach in learning the optimal policy, where the network is conditioned on the weights, i.e, relative importance for the different objectives. To deal with longer action-observation histories, in the case of partially observable environments, we introduce DCRAC-M which uses memory networks to further enhance the reasoning ability of the agent. Experimental evalua...
This thesis proposes some new answers to an old question - how can artificially intelligent agents e...
Many real-world sequential decision making problems are partially observable by nature, and the envi...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (D...
An increasing number of complex problems have naturally posed significant challenges in decision-mak...
Recent advances of actor-critic methods in deep reinforcement learning have enabled performing sever...
Reinforcement learning is the area of machine learning concerned with learning which actions to exec...
We propose a deep reinforcement learning algorithm that employs an adversarial training strategy for...
The field of deep reinforcement learning has seen major successes recently, achieving superhuman per...
We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on...
We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on...
In control, the objective is to find a mapping from states to actions that steer a system to a desir...
Humans live among other humans, not in isolation. Therefore, the ability to learn and behave in mult...
International audienceDespite definite success in deep reinforcement learning problems, actor-critic...
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework b...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
This thesis proposes some new answers to an old question - how can artificially intelligent agents e...
Many real-world sequential decision making problems are partially observable by nature, and the envi...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (D...
An increasing number of complex problems have naturally posed significant challenges in decision-mak...
Recent advances of actor-critic methods in deep reinforcement learning have enabled performing sever...
Reinforcement learning is the area of machine learning concerned with learning which actions to exec...
We propose a deep reinforcement learning algorithm that employs an adversarial training strategy for...
The field of deep reinforcement learning has seen major successes recently, achieving superhuman per...
We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on...
We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on...
In control, the objective is to find a mapping from states to actions that steer a system to a desir...
Humans live among other humans, not in isolation. Therefore, the ability to learn and behave in mult...
International audienceDespite definite success in deep reinforcement learning problems, actor-critic...
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework b...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
This thesis proposes some new answers to an old question - how can artificially intelligent agents e...
Many real-world sequential decision making problems are partially observable by nature, and the envi...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (D...