[PDF] Top 20 Approximate value iteration in the reinforcement learning context. Application to electrical power system control
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Approximate value iteration in the reinforcement learning context. Application to electrical power system control
... of the power system rapidly. The adaptive heuristic critic was used as the basis for optimizing the control parameters through the use of an adaptive critic element ... Voir le document complet
37
Reinforcement learning versus model predictive control: a comparison on a power system problem
... For control problems where a good enough model is avail- able in appropriate form, we, thus, suggest to use the two approaches in ...combination. The fitted Q iteration ... Voir le document complet
13
(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives
... relates to the problem of low-frequency oscil- lations in the system (local modes in the range ...modes in the range ...considered in the ... Voir le document complet
40
2018 — Short term management of hydro-power system using reinforcement learning
... 18 In RL, the environment is commonly explained with a concept of MDP, as this context is also utilized by ...of the specific distinction between the conventional techniques and RL ... Voir le document complet
115
Application of reinforcement learning to electrical power system closed-loop emergency control
... Reinforcement learning techniques are currently being investigated for suitability of use in a wide variety of ...machine learning techniques have been successfully applied to develop ... Voir le document complet
10
Coordination in Distributed Networks via Coded Actions with Application to Power Control
... investigates the problem of coordinating several agents through their actions, focusing on an asymmetric observation structure with two ...knows the past, present, and future realizations of a state that ... Voir le document complet
49
Application of an advanced transient stability assessment and control method to a realistic power system
... on the experience of dispatcher operators. Such practice usually leads to conservative limits and often cannot comply with market requirements to fully exploit power system equipment, ... Voir le document complet
8
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
... performance In Table 1, we present for each map and algorithm the re- spective win-rate by reporting the means that are measured at the end of ...If the algorithms perform equally ... Voir le document complet
8
The value of learning analytics to networked learning on a personal learning environment
... capturing the depth and richness than statistical analysis could ...on the use of analytics; they would like analytics to be used to measure, compare and improve the performance of ... Voir le document complet
12
On the Value Iteration method for dynamic Strong Stackelberg Equilibria
... study the opportunity to use instead the Value Iteration algorithm, the well-known dynamic programming (DP) method used to solve, in particular, Markov Decision ... Voir le document complet
3
Learning in games via reinforcement learning and regularization
... over the entire simplex without becoming infinitely steep at the ...boundary. The basic example here is the squared Euclidean distance under which choice probabilities are determined by a ... Voir le document complet
35
Contributions to Batch Mode Reinforcement Learning
... of the min max problem is far from trivial, even after reformulating the problem so as to avoid the search in the space of all compatible ...functions. To circumvent these ... Voir le document complet
219
CLAMP : application merging in the ECOIN context mediation system using the context linking approach
... To virtually merge Airfare and Car Rental by linking contexts, we take a look at the semantic types that have modifiers and determine the relationships across the two applica[r] ... Voir le document complet
138
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
... Perhaps the most surprising thing is this: running the same algorithm 10 times with the same hyper-parameters using 10 different random seeds and averaging performance over two splits of 5 seeds can ... Voir le document complet
21
Damping control by fusion of reinforcement learning and control Lyapunov functions
... guarantees in the proposed control schemes are ensured by design, ...on the available control choices to the RL ...used. The CLF-based basic control laws for ... Voir le document complet
7
Approximate policy iteration: A survey and some new methods
... of the major approaches for approximate DP, a field that has attracted substantial research interest, and has a wide range of applications, because of its potential to address large and complex ... Voir le document complet
51
An open vibration platform to evaluate postural control using a simple reinforcement learning agent
... 4.1. The Reinforcement Problem In reinforcement learning, the agent learns by evaluating the feedback received from the ...observes the state, select an ... Voir le document complet
8
A reinforcement learning formulation to the complex question answering problem
... a reinforcement learning framework for answering complex ...that the human generated abstract sum- maries are the gold-standard and the users (if they were involved) are satisfied with ... Voir le document complet
23
Learning to Survive: Achieving Energy Neutrality in Wireless Sensor Networks Using Reinforcement Learning
... proposed in the last years to address the non trivial challenge of designing effi- cient adaptation algorithms, suitable for the limited resources provided by sensor nodes in ... Voir le document complet
7
Contributions to deep reinforcement learning and its applications in smartgrids
... addressing the problem of operating an electricity microgrid. The only required assumptions, rather realistic, are that (i) the dynamics of the different constituting elements of the ... Voir le document complet
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