Project title

Automated Gaming Pommerman using Reinforcement Learning

Submitted to:

  • Mr. Cairo Gulati
  • Ms. Shambhavi

Submitted By:

  1. Lonika
  2. Shivani
  3. Anshul Dhull
  4. Barath Mohan
  5. Bhavish Pahwa
  6. Komal Sharma

Project Description

In this project we applied different reinforcement learning algorithms and policies which include imitation learning, DQN, DQFD, and AC3 to the Pommerman FFA competition challenge. We were able to successfully perform as efficiently as SimpleAgent which was a baseline heuristic using DQN and an architecture inspired by AlphaGo and Atari papers. Most of our agents emerged with defensive behaviors where we tried to train them further with reward shaping to observe emergence of other behaviors.

Project Poster

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