[PDF] Top 20 Reinforcement Learning and Dynamic Programming using Function Approximators
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Reinforcement Learning and Dynamic Programming using Function Approximators
... research, and eco- nomics. Automatic control and artificial intelligence are arguably the most important fields of origin for DP and ...linear and stochastic optimal control problems ... Voir le document complet
282
2018 — Short term management of hydro-power system using reinforcement learning
... reward function over the planning ...storage and the electricity production. The function on the side of the value of water is uncertain and nonlinear in the reservoir management problem ... Voir le document complet
115
Direct Value Learning: Reinforcement Learning and Anti-Imitation
... value function associated to the current policy π, defining a new policy from V π , and iterating the process) is known to converge toward a globally optimal policy − usually in a few ...value ... Voir le document complet
22
Sparse Multi-task Reinforcement Learning
... Introduction Reinforcement learning (RL) and approximate dynamic programming (ADP) [26, 3] are effective approaches to solve the problem of decision-making under ...forcement ... Voir le document complet
27
Approximate Dynamic Programming Using Bellman Residual Elimination and Gaussian Process Regression
... approximate dynamic programming and reinforcement learning [2], ...many reinforcement learning ...policy, and the process is ...actions, and therefore can be ... Voir le document complet
7
Learning of Binocular Fixations using Anomaly Detection with Deep Reinforcement Learning
... Reinforcement learning suffers from with the ”curse of ...deep learning field and the arrival of better computers and GPUs, the usual value functions Q, A or V, the policies and ... Voir le document complet
9
Approximate Dynamic Programming Using Bellman Residual Elimination and Gaussian Process Regression
... approximate dynamic programming and reinforcement learning [2], ...many reinforcement learning ...policy, and the process is ...actions, and therefore can be ... Voir le document complet
7
Beyond function approximators for batch mode reinforcement learning: rebuilding trajectories
... with dynamic programming : very problematic because (state-action) value functions need to become functions that take as values “probability distributions of future rewards” and not “expected ... Voir le document complet
33
Improvement of an EVT-based HEV using dynamic programming
... engineers and researchers have proposed different Series-Parallel Hybrid Electric Vehicle SP- HEV ...The dynamic programming method is applied to the THS as well as to a virtual hybrid vehicle with ... Voir le document complet
11
Real-Time Bidding Strategies from Micro-Grids Using Reinforcement Learning
... 1, and α = 0.0005. The consumption and solar pro- duction profiles are considered as in ...price and the quantity of each order are assumed to follow a Gaussian ...hours and the agent can take ... Voir le document complet
4
Learning to Survive: Achieving Energy Neutrality in Wireless Sensor Networks Using Reinforcement Learning
... Fuzzyman and LQ-Tracker, the reader can refer to the respective ...[16] and the other lasting 180 days corresponding to outdoor wind [17], allowing the evaluation of the EM schemes for two different energy ... Voir le document complet
7
Continuous improvement of a document treatment chain using reinforcement learning
... 3 Apprentissage par renforcement Pour atteindre notre objectif, nous appliquons les techniques de l’apprentissage par renforce- ment (Reinforcement Learning, RL). Pour une introduction détaillée au sujet, ... Voir le document complet
13
Accelerating dynamic programming
... We show how this idea can be used for the problem of finding the optimal tree searching strategy in linear time.. This chapter is organized as follows.[r] ... Voir le document complet
136
Deriving divide-and-conquer dynamic programming algorithms using solver-aided transformations
... the Dynamic Program- ming (DP) technique of Richard Bellman [3] to construct an optimal solution to a problem by combining together opti- mal solutions to many overlapping ...overlap and reuse computed ... Voir le document complet
21
Real-Time Reinforcement Learning
... RTAC and SAC in real-time versions of the benchmark ...faster and achieves higher returns than SAC in RTMDP ...policy and value components showing that a big part of RTAC’s advantage over SAC is its ... Voir le document complet
46
On Temporal Aggregators and Dynamic Programming
... function starting from a suitable initial ...aggregators and re- cursive payoffs, and explains why these payoffs could induce different prefer- ...program, and compares it to the optimization ... Voir le document complet
42
Approximate dynamic programming using model-free Bellman Residual Elimination
... The overall complexity of running model-free is still dominated by building the kernel Gram matrix and solving the regression problem, just as in model-based BRE. Fur- thermore, the complexity of solving the ... Voir le document complet
7
Bayesian Nonparametric Inverse Reinforcement Learning
... Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the tran- sition function and a set of observed ... Voir le document complet
17
Focused Crawling through Reinforcement Learning
... run, and selects the most promising link based on this ...states and actions considering both content information and the link ...synchronous and asynchronous ...with and without ... Voir le document complet
17
Deep learning and reinforcement learning methods for grounded goal-oriented dialogue
... questioner and oracle roles, and rewarded the questioner slightly more than the ...other and players were banned after a certain number of ...people and successful dialogues from the ... Voir le document complet
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