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Goals:
- Train an autonomous agent to learn how to play an arcade-based game
- Identify benefits of applying transfer learning to games within the Arcade Learning Environment(ALE) belonging to a similar genre.
Highlights:
- Utilization of open source libraries Open AI Gym and PPO to implement reinforcement learning
- Conversion of game states to a latent space representation for Mario and Contra
- Faster Learning w.r.t Vanilla
- 10% improvement over Vanilla
- Successfully trained an agent for Mario and Contra using PPO algorithm.
- Successfully verified the application of transfer learning approach for games within the same genre.