Deep Reinforcement Learning
Self reflective deep reinforcement learning
... self-reflective deep reinforcement learning; deep learning; actor-critic; neural networks; robot navigation; ...On-line reinforcement learning agents is difficult to ...
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Classifying options for deep reinforcement learning
... Abstract Deep reinforcement learning is the learning of mul- tiple levels of hierarchical representations for re- inforcement ...Hierarchical reinforcement learning focuses on ...
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Deep Reinforcement Learning Pairs Trading
... This research applies a deep reinforcement learning technique, Deep Q-network (DQN), to a stock market pairs trading strategy for profit. Artificial intelligent methods have long since been ...
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Paraphrase Generation with Deep Reinforcement Learning
... Ma et al. (2018) utilize an attention layer instead of a linear mapping in the decoder to pick up word candidates. Iyyer et al. (2018) harness syntac- tic information for controllable paraphrase gen- eration. Zhang and ...
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Deep Reinforcement Learning for Swarm Systems
... in deep reinforcement learning for swarms and multi-agent systems in ...many-agent reinforcement learning platform based on a multi-channel image state representation, which uses ...
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Deep Reinforcement Learning for Drone Delivery
... using deep learning, usually known as deep reinforcement learning or deep ...learning. Deep RL proposes the use of neural networks in the decision ...memory, ...
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The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms
... for learning an ap- proximation of the optimal state-action value function (Q) in model-free Deep Reinforcement Learning ...simultaneously learning an approximation of the state-value ...
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Deep Reinforcement Learning with VizDoomFirst Person Shooter
... Keywords: Deep Reinforcement Learning, VizDoom, First-Person Shooter, DQN, Double Q-learning, Dueling 1 Introduction First-person shooter is a type of video games, in which a computer player ...
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DRLIC: Deep Reinforcement Learning for Irrigation Control
... Agricultural irrigation is a major consumer of freshwater. Current irrigation systems used in field are not efficient, since they are mainly based on soil moisture sensors’ measurement and growers’ experience, but not ...
Towards Informed Exploration for Deep Reinforcement Learning
... Build Order A StarCraft II agent must balance our consumption of resources between many needs, including supply (population capacity), economy, combat units, upgrades, etc. The build order module plays the crucial role ...
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Deep Reinforcement Learning for Interactive Narrative Planning.
... proposed reinforcement learning framework should also work in constructing other intelligent component in interactive narrative systems if properly ...the reinforcement learning framework with ...
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Lenient Multi-Agent Deep Reinforcement Learning
... agent deep reinforcement learning. Much of the success of deep reinforcement learning can be attributed towards the use of experience replay memories within which state ...
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Flow: Deep Reinforcement Learning for Control in SUMO
... solving deep reinforcement problems in traffic that leverages the open-source microsimulator SUMO ...use deep reinforcement learning to develop controllers for a number of intelligent ...
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Lenient multi-agent deep reinforcement learning
... agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through ...
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Experience Selection in Deep Reinforcement Learning for Control
... after learning has ...the reinforcement learning theory, where most methods assume that the Markov decision process is ergodic and that the initial state distribution does not factor into the optimal ...
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On the Expressivity of Neural Networks for Deep Reinforcement Learning
... theoretical gap between mode-based and model-free algo- rithms in the factored MDP setting. Their definition of model-free algorithms requires an exact parameterization: the value-function hypothesis class should be ...
Learning how to Active Learn: A Deep Reinforcement Learning Approach
... of learning active learning strategies from ...active learning corresponds to a sequence of bi- nary annotation decisions applied to a stream of ...active learning algorithm as a policy based ...
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Deep Reinforcement Learning of the Model Fusion with Double Q learning
... intensive learning, and the use of these data for sequential training, and the neural network is ...(deep reinforcement learning) uses different convolutional neural network ...
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Application of Deep Reinforcement Learning in StarCraft II Learning Environment
... 1997, Deep Blue defeated the human world champion in ...using deep reinforcement learning based ...on deep reinforcement learning showed promising ...works, Deep ...
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Robot Navigation in Cluttered Environments with Deep Reinforcement Learning
... of deep reinforcement learning to the prob- lem of robot ...inforcement learning agent knows what direction to drive in order to approach target ...
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