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
23 April 2019Concrete Problems in AI Safety
16 April 2019Constrained Policy Optimization
9 April 2019Reinforcement learning with implicit reward
2 April 2019Lifelong Learning
19 March 2019A Study of AI Population Dynamics with Million-agent Reinforcement Learning