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
16 September 2020Munchausen Reinforcement Learning
18 May 2020The AI Economist
11 May 2020Self-Tuning Deep Reinforcement Learning
27 April 2020Sample Efficiency in RL
20 April 2020Silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors