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
21 October 2020Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess
14 October 2020Multi-agent Social Reinforcement Learning Improves Generalization
7 October 2020Contrastive learning for Dreamer
30 September 2020Phasic Policy Gradient
23 September 2020A Game Theoretic Framework for Model Based Reinforcement Learning