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
2 April 2019Lifelong Learning
19 March 2019A Study of AI Population Dynamics with Million-agent Reinforcement Learning
12 March 2019Neural Architecture Search with Reinforcement Learning
5 March 2019Reward shaping in reinforcement learning
26 February 2019Deep Reinforcement Learning Doesn't Work Yet