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Weekly review of Reinforcement Learning papers #9
Every Monday, I present 4 publications from my research area. Let’s discuss them!
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Paper 1: Value Iteration in Continuous Actions, States and Time
Lutter M., Mannor S., Peters J., Fox D., Garg A. (2021). Value Iteration in Continuous Actions, States and Time. arXiv preprint arXiv:2105.04682.
Reinforcement learning methods were first tabular: one had to choose an action among a finite number of actions, resulting from an observation from a finite number of possible observations. These learning methods were successively extended to continuous observation spaces and then to continuous action spaces. The authors are interested here in continuous time. It is not a question of taking an action every t seconds, but of carrying out a real control in continuous time.
The most obvious framework is that of robotics, and this is the one they have chosen. They propose the continuous Fitted Value Iteration (cFVI), an algorithm that allows a continuous control, based on a known dynamic model.
The authors show the efficiency of the approach in several control environments, both in simulation and in real world. One of the…
