... of **Q**-**learning** algorithms and showing their dependence on the **learning** ...synchronous **Q**-**learning**. We show that for a polynomial **learning** rate we have a complexity, which is ...

... reinforcement **learning** algorithms, and the goal of reinforcement **learning** [1, 2] is a good strategy for **learning** continuous decision-making problems by optimizing the accumulated future reward ...

... A **Q**-**Learning** agent learns how to address uncertain decision- making problems with dynamic environments ...tabular-**Q** **learning** requires iterative updating to converge, an optimal policy is ...

... Multi **Q**-**learning** algorithms using the same environment parameters as Figure ...Multi **Q**-**learning** algorithm; the value estimates quickly converge to the true value of the state, ...of ...

... reinforcement **learning** problems. Reinforce- ment **learning** problems require improvement of behaviour based on received ...rewards. **Q** -**Learning** has the potential to reduce robot programming ...

... Using a neural network as a function approximator for the **Q**-values has shown unstable behaviour and might lead to divergence [13]. One step for overcoming this problem is to use experience replay [14] in which the ...

... making. **Deep** **learning** provides us an effective way to understand big data with a ...A **Q**-**learning** based moving object recognition approach, which firstly finds out moving object region and then ...

... The **Q**-**learning** algorithm converges to an optimal policy that maximizes rewards given by the environment on the ...reinforcement **learning** algorithms, the agent receives a reward of value 1 if the task ...

... standard **Q**-**learning** with the eligibilities set to zero for non-policy actions, means the eligibilities are only allowed to build up when the robot takes a sequence of greedy policy ...standard ...

... ment **learning** (RL) to approximate dynamic oracles for transition systems where exact dy- namic oracles are difficult to ...reinforcement **learning** problem, design the reward function inspired by the ...

... novel **deep** reinforcement **learning**-based algo- rithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding ...use **Deep** **Q**-**Learning** ...

... Proposed approach makes use of sequence-to-sequence framework [9]. The model is based on Recurrent Neural Network (RNN) which reads input at one token at a time while generating output at one token at time. To speedup ...

... 5000. The target value function is updated at the end of each epoch. In each epoch, **Q**(.) and M (.) are refined using one-step (Z = 1) 16-tuple- minibatch update. 4 In planning, the maximum length of a simulated ...

... The **deep**, surface, and strategic approaches to **learning** are among the basic approaches in ...The **deep** approach aims at real understanding plus long-term and significant **learning** of materials, ...

... machine **learning**, bolster vector machines SVMs, likewise bolster vector networks are directed **learning** models with related **learning** calculations that dissect information utilized for characterization ...

... paper **Deep** **learning** for noise-tolerant RDFS reasoning by Bassem Makni and James Hendler presents a noise-tolerant RDFS reasoning approach building on neural machine ...

... as activation functions in the hidden layers of the proposed deep learning model for learning actual.. 151[r] ...

... The Agriculture sector in India is declining day by day which affects the production capacity of the ecosystem. There is an urgent need to solve the problem in the domain to restore vibrancy and put it back on higher ...

... The study provided no definite evidence of any association between students’ experience of studying the study-management component of the TPP and changes in their study-management-related skills. However, there are ...

... These methods have high compression errors and low compression ratios. [10-12] The **deep** neural networks (DNNs) have the demand on quality analysis. DNNs consits millions of parameters in an unparalleled ...