DIAMBRA Arena comes with a large number of ready-to-use wrappers and examples showing how to apply them. They cover a wide spectrum of use cases, and also provide reference templates to develop custom ones. In order to activate wrappers, one needs to properly set the WrapperSettings class attributes and provide them as input to the environment creation method, as shown in the next code block. The class attributes are described in the next sections below.
from diambra.arena import EnvironmentSettings, WrapperSettings
# Settings specification
settings = EnvironmentSettings()
# Wrapper settings specification
wrapper_settings = WrapperSettings()
wrapper_settings.setting_1 = value_1
wrapper_settings.setting_2 = value_2
env = diambra.arena.make("doapp", settings, wrapper_settings)
Implementation examples and templates can be found in the code repository, here.
| Name | Type | Default Value(s) | Value Range | Description |
|---|---|---|---|---|
no_attack_buttons_combinations | bool | False | True / False | Restricts the number of attack actions to single buttons, removing attack buttons combinations |
wrappers_settings.no_attack_buttons_combinations = True
| Name | Type | Default Value(s) | Value Range | Description |
|---|---|---|---|---|
no_op_max | int | 0 | [0, 12] | Performs a maximum of N No-Action steps after episode reset |
wrappers_settings.no_op_max = 6
DEPRECATED: For speed, consider using the environment setting frame_shape that performs the same operation on the engine side, as described in this section.
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
frame_shape | tuple of three int (H, W, C) | (0, 0, 0) | H, W: [0, 512] C: 0 or 1 | frame | If active, resizes the frame and/or converts it from RGB to grayscale. Combinations: (0, 0, 0) - Deactivated; (H, W, 0) - RBG frame resized to H X W; (0, 0, 1) - Grayscale frame; (H, W, 1) - Grayscale frame resized to H X W; Keeps observation element of type Box, changes its shape |
wrappers_settings.frame_shape = (128, 128, 1)
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
stack_frames | int | 1 | [1, 48] | frame | Stacks latest N frames together along the third dimension. Keeps observation element of type Box, changes its shape |
dilation | int | 1 | [1, 48] | frame | Builds frame stacks adding one every N frames. Keeps observation element of type Box |
wrappers_settings.stack_frame = 4
wrappers_settings.dilation = 1
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
add_last_action | bool | False | True / False | action | Adds latest action to the observation space dictionary, under the key action.Adds an observation element of type Discrete or MultiDiscrete depending on the action space |
wrappers_settings.add_last_action = True
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
stack_actions | int | 1 | [1, 48] | action | Stacks latest N actions together. Changes observation element type from Discrete or MultiDiscrete (depending on the action space) to MultiDiscrete, having N elements, each with cardinality equal to the original set(s) of the action space |
wrappers_settings.stack_actions = 12
For the Stack Actions wrapper to to be applied, the Add Last Action one must be activated.
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
scale | bool | False | True / False | All | Activates observation scaling. Affects observation elements as follows: - Box: type kept, changes bounds to be between 0 and 1. - Discrete (Binary): depends on process_discrete_binary (see below).- Discrete: changed to MultiBinary through one-hot encoding, size equal to original Discrete cardinality. - MultiDiscrete: changed to N-MultiBinary through N-times one-hot encoding, N equal to original MultiDiscrete size, encoding size equal to original MultiDiscrete element cardinality |
exclude_image_scaling | bool | False | True / False | Frame | If True, prevents scaling to be applied on the game frame |
process_discrete_binary | bool | False | True / False | Discrete Binary | Controls how Discrete (Binary) observations are treated: - False: not affected; - True: changed to MultiBinary through one-hot encoding, size equal to 2 |
wrappers_settings.scale = True
wrappers_settings.exclude_image_scaling = True
wrappers_settings.process_discrete_binary = True
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
role_relative | bool | False | True / False | Player specific observation | Renames observation depending on the role played by the agent. For example: if the agent is playing with P1 role, P1 key becomes own and P2 key becomes opp, thus obs["P1"]["key_1"] becomes obs["own"]["key_1"] and obs["P2"]["key_1"] becomes obs["opp"]["key_1"] |
wrappers_settings.role_relative = True
| Name | Type | Default Value(s) | Value Range | Target Observation Element | Description |
|---|---|---|---|---|---|
flatten | bool | False | True / False | Observation | Activates observation dictionary flattening, removing nested dictionaries and creating new keys joining original ones using “_” across nesting levels. For example: obs["P1"]["key_1"] becomes obs["P1_key_1"] |
filter_keys | list of str | [] | - | Observation except frame | Defines the list of RAM states to keep in the observation space |
wrappers_settings.flatten = True
wrappers_settings.filter_keys = ["stage", "P1_health", "P2_char"]
The two operations are applied simultaneously, thus for filtering to be applied, flattening must be activated.
| Name | Type | Default Value(s) | Value Range | Description |
|---|---|---|---|---|
repeat_action | int | 1 | [1, 12] | Keeps repeating the same action for N environment steps |
In order to use this wrapper, the step_ratio setting must be set to equal to 1.
wrappers_settings.repeat_action = 4
| Name | Type | Default Value(s) | Value Range | Description |
|---|---|---|---|---|
normalize_reward | bool | False | True / False | Activates reward normalization. Divides reward value by the product between the normalization_factor value (see next row) and the delta between maximum and minium health bar value for the given game |
normalization_factor | float | 0.5 | [-inf, +inf] | Defines the normalization factor multiplying the delta between maximum and minium health bar value for the given game |
wrappers_settings.normalize_reward = True
wrappers_settings.normalization_factor = 0.5
| Name | Type | Default Value(s) | Value Range | Description |
|---|---|---|---|---|
clip_reward | bool | False | True / False | Activates reward clipping. Applies the sign function to the reward value |
wrappers_settings.clip_reward = True
| Name | Type | Default Value(s) | Value Range | Description |
|---|---|---|---|---|
wrappers | list of pairs (list) of [WrapperClass, kwargs] | [] | - | Applies in sequence the list of wrappers classes with their correspondend settings |
wrappers_settings.wrappers = \
[[CustomWrapper1, {"setting1_1": value1_1,"setting1_2": value1_2}], \
[CustomWrapper2, {"setting2_1": value2_1,"setting2_2": value2_2}]]
The custom wrappers are applied after all the activated built-in ones, if any.