peer reviewedModel predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained
Solving complex optimal control problems have confronted computational challenges for a long time. R...
This article investigates synthetic model-predictive control (MPC) problems to demonstrate that an i...
A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in which the envi...
peer reviewedThis paper compares reinforcement learning (RL) with model predictive control (MPC) in ...
Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without a...
Model predictive control (MPC) offers an optimal control technique to establish and ensure that the ...
Despite the success of reinforcement learning (RL) in various research fields, relatively few algori...
We propose the use of Model Predictive Control (MPC) for controlling systems described by Markov dec...
In this paper we propose a new approach to complement reinforcement learning (RL) with model-based c...
Model predictive control (MPC) is becoming an increasingly popular method to select actions for cont...
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, espec...
Autonomous systems extend upon human capabilities and can be equipped with superhuman attributes in ...
Reinforcement learning (RL) is a machine learning method that has recently seen significant research...
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, espec...
In this paper we propose and compare methods for combining system identification (SYSID) and reinfor...
Solving complex optimal control problems have confronted computational challenges for a long time. R...
This article investigates synthetic model-predictive control (MPC) problems to demonstrate that an i...
A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in which the envi...
peer reviewedThis paper compares reinforcement learning (RL) with model predictive control (MPC) in ...
Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without a...
Model predictive control (MPC) offers an optimal control technique to establish and ensure that the ...
Despite the success of reinforcement learning (RL) in various research fields, relatively few algori...
We propose the use of Model Predictive Control (MPC) for controlling systems described by Markov dec...
In this paper we propose a new approach to complement reinforcement learning (RL) with model-based c...
Model predictive control (MPC) is becoming an increasingly popular method to select actions for cont...
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, espec...
Autonomous systems extend upon human capabilities and can be equipped with superhuman attributes in ...
Reinforcement learning (RL) is a machine learning method that has recently seen significant research...
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, espec...
In this paper we propose and compare methods for combining system identification (SYSID) and reinfor...
Solving complex optimal control problems have confronted computational challenges for a long time. R...
This article investigates synthetic model-predictive control (MPC) problems to demonstrate that an i...
A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in which the envi...
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