Wrappers Options

import diambra.arena

if __name__ == '__main__':

    # Gym wrappers settings
    wrappers_settings = {}

    # Number of no-Op actions to be executed
    # at the beginning of the episode (0 by default)
    wrappers_settings["no_op_max"] = 0

    # Number of steps for which the same action should be sent (1 by default)
    wrappers_settings["sticky_actions"] = 1

    # Frame resize operation spec (deactivated by default)
    # WARNING: for speedup, avoid frame warping wrappers,
    #          use environment's native frame wrapping through
    #          "frame_shape" setting (see documentation for details).
    wrappers_settings["hwc_obs_resize"] = (128, 128, 1)

    # Wrapper option for reward normalization
    # When activated, the reward normalization factor can be set (default = 0.5)
    # The normalization is performed by dividing the reward value
    # by the product of the factor times the value of the full health bar
    # reward = reward / (C * fullHealthBarValue)
    wrappers_settings["reward_normalization"] = True
    wrappers_settings["reward_normalization_factor"] = 0.5

    # If to clip rewards (False by default)
    wrappers_settings["clip_rewards"] = False

    # Number of frames to be stacked together (1 by default)
    wrappers_settings["frame_stack"] = 4

    # Frames interval when stacking (1 by default)
    wrappers_settings["dilation"] = 1

    # How many past actions to stack together (1 by default)
    wrappers_settings["actions_stack"] = 12

    # If to scale observation numerical values (deactivated by default)
    # optionally exclude images from normalization (deactivated by default)
    # and optionally perform one-hot encoding also on discrete binary variables (deactivated by default)
    wrappers_settings["scale"] = True
    wrappers_settings["exclude_image_scaling"] = True
    wrappers_settings["process_discrete_binary"] = True

    # Scaling interval (0 = [0.0, 1.0], 1 = [-1.0, 1.0])
    wrappers_settings["scale_mod"] = 0

    # Flattening observation dictionary and filtering
    # a sub-set of the RAM states
    wrappers_settings["flatten"] = True
    wrappers_settings["filter_keys"] = ["stage", "P1_ownSide", "P1_oppSide",
                                        "P1_ownHealth", "P1_oppChar",
                                        "P1_actions_move", "P1_actions_attack"]

    env = diambra.arena.make("doapp", {}, wrappers_settings)

    observation = env.reset()
    env.show_obs(observation)

    while True:

        actions = env.action_space.sample()
        print("Actions: {}".format(actions))

        observation, reward, done, info = env.step(actions)
        env.show_obs(observation)
        print("Reward: {}".format(reward))
        print("Done: {}".format(done))
        print("Info: {}".format(info))

        if done:
            observation = env.reset()
            env.show_obs(observation)
            break

    env.close()