mortal-kombat-3-ppo-diambra

Model Card for Model ID

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A DRL agent playing Ultimate Mortal Kombat 3 trained using diambra ai library

Codes

Github repos(Give a star if found useful):

• https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
• https://github.com/hishamcse/DRL-Renegades-Game-Bots
• https://github.com/hishamcse/Robo-Chess

Model Details

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• My Code for this model: https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots/tree/main/VII%20-%20Diambra_AI_Ultimate-Mortal-Kombat-3
• Tutorial: https://github.com/alexpalms/deep-rl-class/blob/main/units/en/unitbonus3
• Documentation: https://docs.diambra.ai/

Training Details

Training Hyperparameters

  folders:
    parent_dir: "./results/"
    model_name: "sr6_128x4_das_nc"
  
  settings:
    game_id: "umk3"
    step_ratio: 6
    frame_shape: !!python/tuple [128, 128, 1]
    continue_game: 0.0
    action_space: "discrete"
    characters: "Skorpion"
    difficulty: 5
  
  wrappers_settings:
    normalize_reward: true
    no_attack_buttons_combinations: true
    stack_frames: 4
    dilation: 1
    add_last_action: true
    stack_actions: 12
    scale: true
    exclude_image_scaling: true
    role_relative: true
    flatten: true
    filter_keys: ["action", "own_health", "opp_health", "own_side", "opp_side", "opp_character", "stage", "timer"]
  
  policy_kwargs:
    #net_arch: [{ pi: [64, 64], vf: [32, 32] }]
    net_arch: [256, 256]
    activation_fn: "leaky_relu"
  
  ppo_settings:
    gamma: 0.94
    model_checkpoint: "660000"     # 0: No checkpoint, else: Load checkpoint (if previously trained)
    learning_rate: [1.0e-3, 2.5e-6] # To start
    clip_range: [0.3, 0.015] # To start
    batch_size: 512 #8 #nminibatches gave different batch size depending on the number of environments: batch_size = (n_steps * n_envs) // nminibatches
    n_epochs: 14
    n_steps: 512
    gae_lambda: 0.9520674913500098
    ent_coef: 2.361611947920214e-06
    vf_coef: 0.6420316461542878
    autosave_freq: 50000
    time_steps: 1000000