Add to ORKGContinuous state spaces and stochastic, switching dy-namics characterize a number of rich, real-world do-mains, such as robot navigation across varying ter-rain. We describe a reinforcement-learning algorithm for learning in such domains. We prove that this algo-rithm is provably approximately correct for certain en-vironments. Unfortunately, no optimal planning tech-niques exist in general for such problems; we instead use fitted value iteration to solve the learned MDP, and extend the error bounds from prior work on policy performance to include the error due to approximate planning. Finally, we provide a robotic car experiment over varying terrain to demonstrate that these dynam-ics representations adequately capture real world dy-namics...
Reinforcement Learning (RL) in finite state and action Markov Decision Processes is studied with an ...
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usua...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
To quickly achieve good performance, reinforcement-learning algorithms for acting in large continuou...
Dynamic control tasks are good candidates for the application of reinforcement learning techniques. ...
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
Abstract. The behavior of reinforcement learning (RL) algorithms is best understood in completely ob...
The behaviour of reinforcement learning (RL) algorithms is best understood in completely observable,...
We present a data-efficient reinforcement learning method for continuous state-action systems under ...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
Trial and error learning methods are often ineffective when applied to robots. This is due to certa...
People learn skills by interacting with their surroundings from the time of their birth. Reinforceme...
Reinforcement learning for robot control tasks in continuous environments is a challenging problem d...
Reinforcement Learning (RL) in finite state and action Markov Decision Processes is studied with an ...
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usua...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
To quickly achieve good performance, reinforcement-learning algorithms for acting in large continuou...
Dynamic control tasks are good candidates for the application of reinforcement learning techniques. ...
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
Abstract. The behavior of reinforcement learning (RL) algorithms is best understood in completely ob...
The behaviour of reinforcement learning (RL) algorithms is best understood in completely observable,...
We present a data-efficient reinforcement learning method for continuous state-action systems under ...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
Trial and error learning methods are often ineffective when applied to robots. This is due to certa...
People learn skills by interacting with their surroundings from the time of their birth. Reinforceme...
Reinforcement learning for robot control tasks in continuous environments is a challenging problem d...
Reinforcement Learning (RL) in finite state and action Markov Decision Processes is studied with an ...
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usua...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...