Reinforcement learning with implicit reward
In some cases, it is hard to design an appropriate reward function for a specific aim in the reinforcement learning environment.
At the seminar, we will discuss an approach of implicit reward design that uses expert-defined trajectories. We will present experimental results in Atari games and MuJoCo simulator.
Speaker: Mikhail Shavkunov.
Presentation language: Russian.
Date and Time: April 9th, 18:30-20:00.
Place: Times, room 204.
Videos from previous seminars are available at http://bit.ly/MLJBSeminars
- About seminars
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
13 April 2020AlphaGo to MuZero. Победа компьютера над человеком в интеллектуальных играх.