With AndroidEnv we provide a mechanism for easily defining RL tasks for the agent to learn. This includes various types of information such as what app/game it should train on, what rewards the environment returns, or the start state distribution and the episode end criteria.
A task definition is captured in the form of a Task()
proto message. These
are most easily created by writing a .textproto
file, then parsing it into a
proto message. In this section you can find a detailed description about the
types of information that make up a task, and an example demonstrating exactly
how to put these into code.
Expand this tab to view the main types of information captured in these messages:
-
id
: An ID used to identify the task. -
setup_steps
: These are steps the environment will perform right after launching the simulator. Possible steps include:install_apk
: Installs an application from a specified path to the APK file.start_activity
: Launches the requested app/activity.rotate
: Sets the orientation of the device (landscape/portrait).
-
reset_steps
: These are steps the environment will perform right at the beginning of a new RL episode. Possible steps include:force_stop
: Stops a given app.start_activity
: Launches the requested app/activity.start_screen_pinning
: Restricts the agent's interaction to a particular activity through screen pinning, meaning the agent will not be able to quit the given app.clear_cache
: Clears the cache of a given app.
-
success_conditions
: For each success condition defined, the environment will make sure that these conditions were met after finishingsetup_steps
andreset_steps
. They might include conditions such as:check_install
: Makes sure that the request app was successfully installed.wait_for_app_screen
: Waits until the request app was successfully launched.
-
expected_app_screen
: If this value is set to a particular activity, the environment will periodically check if the agent is still interacting with said activity, making sure it has not accidentally quit the application we want it to be training on. -
max_episode_sec
: Puts a time limit on the episodes, triggering an episode reset if the current episode has lasted too long. -
max_duration_steps
: Puts a step limit on the episodes, triggering an episode reset once the agent has reached the specified limit. -
log_parsing_config
: If the environment is parsing logcat messages, this field will determine what information it should listen for using regular expressions.filters
: The environment filters log messages for these labels which signify that such messages were meant to be parsed by AndroidEnv.log_regexps
: Once a log message was identified as relevant using the filters, the environment parses its contents using these regular expressions. For example, an application might be sending log messages of the formreward: 1.0
, then the task will capture this info using the regexp^[Rr]eward: ([-+]?[0-9]*\\.?[0-9]*)$
.
Expand this tab to see what an example `.textproto` file might look like in practice:
id: "classic_2048"
name: "Classic 2048 - Default"
description: "Slide numbered tiles on a grid to combine them to create a tile with the number 2048"
package_name: "com.tpcstld.twozerogame"
full_activity_name: "com.tpcstld.twozerogame/com.tpcstld.twozerogame.MainActivity"
# Perform these upon launching the environment
setup_steps: [
{
# Install the 2048 app
adb_call: {
install_apk: {
filesystem: {
path: path/to/classic_2048.apk
}
}
}
# Check if it was installed correctly
success_condition: {
check_install: {
package_name: "com.tpcstld.twozerogame"
timeout_sec: 10.0
}
}
},
# Orient the screen in portait mode
{ adb_call: { rotate: { orientation: PORTRAIT_0 } } }
]
# Perform these upon episode resets
reset_steps: [
# Stop the 2048 app
{ adb_call: { force_stop: { package_name: "com.tpcstld.twozerogame" } } },
{ adb_call: { clear_cache: { package_name: "com.tpcstld.twozerogame" } } },
# Start the 2048 app
{
adb_call: {
start_activity: {
full_activity: "com.tpcstld.twozerogame/com.tpcstld.twozerogame.MainActivity"
extra_args: [
"--ez", '"RL_TASK_ENABLED"', '"true"',
"--es", '"RL_TASK_GAME_CONFIG"', '"{}"'
]
}
}
# Wait until the app has launched successfully
success_condition: {
wait_for_app_screen: {
app_screen: {
activity: "com.tpcstld.twozerogame/com.tpcstld.twozerogame.MainActivity"
view_hierarchy_path: [
]
}
timeout_sec: 10.0
}
num_retries: 10
}
},
# Make sure the agent cannot quit the 2048 app
{
adb_call: {
start_screen_pinning: {
full_activity: "com.tpcstld.twozerogame/com.tpcstld.twozerogame.MainActivity"
}
}
}
]
# Periodically check if the agent has accidentally quit the app
expected_app_screen: {
activity: "com.tpcstld.twozerogame/com.tpcstld.twozerogame.MainActivity"
view_hierarchy_path: []
}
max_episode_steps: 500
# Capture expected format of log messages
log_parsing_config: {
filters: ["AndroidRLTask:V"]
log_regexps: {
score: "^[Ss]core: ([-+]?[0-9]*\\.?[0-9]*)$"
reward: "^[Rr]eward: ([-+]?[0-9]*\\.?[0-9]*)$"
episode_end: "^episode[ _]end$"
extra: "^extra: (?P<name>[^ ]*)[ ]?(?P<extra>.*)$"
json_extra: "^json_extra: (?P<json_extra>.*)$"
}
}
You might have noticed that tasks often rely on log messages exposed by the Android system, which AndroidEnv can intercept and translate into items such as rewards, episode end signals or task extras.
One way to define rewards is by using
log_parsing_config.LogRegexps.RewardEvent
messages in the task proto. These
consist of a regular expression and a numeric value indicating the intended
reward. If the regexp is matched in any of the lines of the logcat stream, the
agent will receive the given reward. It is also possible to have multiple of
these RewardEvents, allowing us to give rewards for different log messages. The
same applies for episode end signals: logcat messages that match the regexps
defined in log_parsing_config.LogRegexps.episode_end
will trigger an episode
reset.
Of course, applications might not send suitable messages by default, so in order to have access to such messages, we often add them to the apps' source code to match our expectations. For example, in the case of the 2048 app, we find in the game's source code the exact lines where the score is computed, and add a line to log this value in the format that is expected by the textproto (or conversely, make sure the textproto matches the format you specified here). For example:
// Make sure thet LOG_FILTER matches 'filters' in the textproto
public static final String LOG_FILTER = "AndroidRLTask";
// Make sure that the corresponding part of 'log_regexps' will match this string
Log.i(LOG_FILTER, String.format(Locale.ENGLISH, "reward: %r", reward_value))
You can take a look at example APKs extended with log messages in the example tasks (see the section below).
Along with the environment implementation we provide a set of example task definitions. These were chosen so that they would demonstrate the large variety of different challenges (e.g. app navigtion, puzzle games, time-reactive games, adventure games, card games...) and corresponding interfaces (e.g. button pressing, swiping, drag-and-drop...) available in AndroidEnv. You can find a list and detailed description of each of these tasks in example_tasks.md.