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agent.py
agent.py
 
 
best_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_error_u.pkl
best_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_error_u.pkl
 
 
best_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_random.pkl
best_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_random.pkl
 
 
game.py
game.py
 
 
gif_maker.ipynb
gif_maker.ipynb
 
 
model.py
model.py
 
 
ohe_2_steps_move_penalty_log_reward_512_3_layers_random_short.gif
ohe_2_steps_move_penalty_log_reward_512_3_layers_random_short.gif
 
 
training.ipynb
training.ipynb
 
 
worst_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_error_u.pkl
worst_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_error_u.pkl
 
 
worst_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_random.pkl
worst_game_history_ohe_2_steps_move_penalty_log_reward_512_3_layers_random.pkl
 
 

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Teaching a reinforcement learning agent to play 2048 puzzle game

Group Members :

  1. Vamshi Duvva
  2. Sai Manideep B
  3. S.Varshith Reddy
  4. Abhigna Srikala
  5. Guru Vardhan.k

To obtain the results , all constraints on the agent were defined and the environment is set and all that is required is to run the training.ipynb file.

Thank you

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