Constrained Policy Optimization
A need to limit agent's action often arise in reinforcement learning, i.e. from the point of safety, when we are dealing with human-AI interactions.
At the seminar, we will discuss a recent paper "Constrained Policy Optimization", that adapts trust region optimization for constrained MDP to guarantee constraints on every step of the training process.
Speaker: Ilya Kaysin.
Presentation language: Russian.
Date and Time: April 16th, 18:30-20:00.
Place: Times, room 204.
Videos from previous seminars are available at http://bit.ly/MLJBSeminars
6 April 2020Agent57: Outperforming the Atari Human Benchmark
23 March 2020Dream To Control
16 March 2020Почему иерархическое обучение (иногда) работает?
2 March 2020Model Based RL для игр Atari
17 February 2020A Survey and Critique of Multiagent Deep Reinforcement Learning