MLMU KE: Introduction to Deep Reinforcement Learning II

Pozývame Vás na pokračovanie úvodu do reinforcement learningu v podaní Slava Maťašovského zo spoločnosti Tachyum. Stretneme sa 26. 11. v UVP Technicom o 18:00.

workshop description:

1) From Reinforcement Learning to Deep Reinforcement Learning

2) Value Based Methods

-Deep Q-Learning Agent

-Dueling Deep Q-Learning Agent

3) Policy Gradient Methods

-Introduction to PG methods - state action-value and policy parameterization using DNN - Deep Neural Networks

-Policy Gradient Theorem

-A2C - Advantage Actor-Critic Agents

-DDPG - Deep Deterministic Policy Gradient Agent

-MADDPG - Multi-Agent Deep Deterministic Policy Gradient

-SAC - Soft Actor Critic Agent

4) Stabilizing Deep Reinforcement Learning

-Experience Replay

-Fixed Q-Targets

5) Examples of RL agents using ML Agents environment

-Car simulator using SAC/DDPG

-Table tennis multiplayer game using MADDPG

-Robotic arm object fetching

speaker bio:

Slavo Matašovský vyštudoval inžinierský titul v odbore computer science na Technickej univerzite v Košiciach. Aktuálne pracuje vo firme Tachyum (www.tachyum.com) ako senior AI Architect.

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