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
19 February 2019Multi-agent reinforcement learning with populations dynamic
12 February 2019Cross-domain knowledge transfer in policy gradient methods
5 February 2019Superpositional linear-nonlinear modelling of multi-agent systems by tropical algebra methods
29 January 2019Deep Multi-agent Reinforcement Learning
22 January 2019Predicting of people trajectories for a safer robot movement