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Self Play Actor Critic, Reinforcement Learning on TROY; all puns intended

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ViRu-ThE-ViRuS/desTROY

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desTROY

Learning to play the TROY game using reinforcement learning methods like policy gradients actor critic models, Deep Q Learning, Deep Deterministic Actor Critic, and Advanced Advantage Actor Critic, and technologies like Pytorch and Cython.

results

gen0

starting training on Actor Critic model

gen1

after 3 hours of training, the model shows significant recognition of game boundaries and tries to avoid itself by going in circles, using the actor critic model

  • the leads are promising, but frankly, the training time for this algorithm is too slow for me, so i will be coming back to this problem to explore A3C, DDPG learning methods later

gen2

after 1 hour of training on dueling double deep q network, results are much better even though each episode is taking longer to run (due to bigger batches in the replay buffer)

  • trying to speed up learning by giving incentive rewards for survival and strategic cutting off of opponent
  • this gif is from training phase where i removed the opponent, to see if model converges faster... since im using self play, both players show this learned knowledge when in dueling mode

future work

  • add more algorithms
  • explore evolutionary strategy in this particular game (integrate my Evolve project with desTROY)

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Self Play Actor Critic, Reinforcement Learning on TROY; all puns intended

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