myrtle 0.0.3

A benchmarking framework for real-time RL with multiple intermittent rewards

Myrtle

A real-time reinforcement learning benchmark framework that allows for multiple and intermittent rewards.

Myrtle connects a real-time environment with an agent via a Queue and runs them.

• Getting Started
• Creating Worlds
• Creating Agents

Heads Up

Myrtle runs on Linux only. It is not compatible with Windows or MacOS due to the fact that it starts new processes with os.fork(). Forking behavior is detailed here.

Getting started

Install for off-the-shelf use

python3 -m pip install myrtle

Install for editing

git clone https://codeberg.org/brohrer/myrtle.git

python3 -m pip install pip --upgrade
python3 -m pip install --editable myrtle

Using Myrtle

To run a RL agent against a world

from myrtle import bench
bench.run(AgentClass, WorldClass)

For example, to run a Random Single Action agent with a Stationary Multi-armed Bandit

from myrtle import bench
from myrtle.agents.random_single_action import RandomSingleAction
from myrtle.worlds.stationary_bandit import StationaryBandit
bench.run(RandomSingleAction, StationaryBandit)

Project layout

src/
    myrtle/
        bench.py
        agents/
            base_agent.py
            ...
        worlds/
            base_world.py
            ...
tests/
    README.md
    base_agent_test.py
    base_world_test.py
    bench_test.py
    ...

The run() function in bench.py is the entry point.

Run the test suite with pytest.

Worlds

To be compatible with the Myrtle benchmark framework, world classes have to have a few characteristics. There is a skeleton implementation in base_world.py to use as a starting place.

Attributes

• n_sensors: int, a member variable with the number of sensors, the size of the array that the world will be providing each iteration.
• n_actions: int, a member variable with the number of actions, the size of the array that the world will be expecting each iteration.
• n_rewards (optional): int, a member variable with the number of rewards, the length of the reward list that the world will be providing each iteration. If not provided, it is assumed to be the traditional 1.
• name: str, an identifier so that the history of runs on this world can be displayed together and compared against each other.

Multiple and intermittent rewards

Having the possibility of more than one reward is a departure from the typical RL problem formulation and, as far as I know, unique to this framework. It allows for intermittent rewards, that is, it allows for individual rewards to be missing on any given time step. See page 10 of this paper for a bit more context

Initialization

A World class should be initializable with a two keyword arguments, multiprocessing Queues, sensor_q and action_q.

sensor_q is the Queue for passing messages from the world to the agent. It provides sensor and reward information, as well as information about whether a the current episode has terminated, or the world has ceased to exist altogether. More detail here.

action_q is the Queue for passing messages from the agent to world. It informs the world of the actions the agent has chosen to take. More detail here.

Methods

Every World contains a run() method. This is what the benchmark calls. It will determine how long the World runs, how many many times it starts over at the beginning (episodes), and everything else about what is run during benchmarking:

Real-time

A good world for benchmarking with Myrtle will be tied to a wall clock in some way. In a perfect world, there is physical hardware involved. But this is expensive and time consuming, so more often it is a simulation of some sort. A good way to tie this to the wall clock is with a pacemaker that advances the simluation by one time step at a fixed cadence. There exists such a thing in the pacemaker package (pip install pacemaker-lite).

The sample worlds in this package all have the import line

from pacemaker.pacemaker import Pacemaker

and use the Pacemaker.beat() method to keep time.

BaseWorld

There is a base implementation of a world you can use as a foundation for writing your own. Import and extend the BaseWorld class.

from myrtle.worlds.base_world import BaseWorld

class MyWorld(BaseWorld):
    ...

It takes care of the interface with the rest of the benchmarking platform, including process management, communication, and logging. To make it your own, override the __init__(), reset(), and step() methods.

Stock Worlds

In addition to the base world there is a very short, but growing list of sample worlds that come with Myrtle. They are useful for developing, debugging, and benchmarking new agents.

• Stationary Bandit
  from myrtle.worlds.stationary_bandit import StationaryBandit
  A multi-armed bandit where each arm has a different maximum payout and a different expected payout.

• Non-stationary Bandit
  from myrtle.worlds.nonstationary_bandit import NonStationaryBandit
  A multi-armed bandit where each arm has a different maximum payout and a different expected payout, and after a number of time steps the max and expected payouts change for all arms.

• Intermittent-reward Bandit
  from myrtle.worlds.intermittent_reward_bandit import IntermittentRewardBandit
  A stationary multi-armed bandit where each arm reports its reward individually but with intermittent outages.

Agents

An Agent class has a few defining characteristics. For an example of how these can be implemented, check out base_agent.py.

Initialization

An Agent initializes with at least these named arguments, the same as described above for Worlds.

• n_sensors: int
• n_actions: int
• n_rewards (optional): int
• sensor_q: multiprocessing.Queue
• action_q: multiprocessing.Queue

Other attributes

The only other attribute an Agent is expected to have is a name.

• name: str, an identifier so that the history of runs with this agent can be displayed together and compared against each other.

run() method

Every Agent contains a run() method. This is the method that gets called by the benchmarking code. By convention the run() method runs on an infinite loop, at least until it receives the message from the World that its services are no longer needed.

BaseAgent

There is a base implementation of an agent you can use as a foundation for writing your own. Import and extend the BaseAgent class.

from myrtle.agents.base_agent import BaseAgent

class MyAgent(BaseAgent):
    ...

It takes care of the interface with the rest of the benchmarking platform, including process management, communication, and logging. To make it your own, override the __init__(), reset(), and step() methods.

Agents included

As of this writing there is a short list of agents that come with Myrtle. These aren't intended to be very sophisticated, but they are useful for providing performance baselines, and they serve as examples of how to effectively extend the BaseAgent.

• Random Single Action
  from myrtle.agents.random_single_action import RandomSingleAction
  An agent that selects one action at random each time step.

• Random Multi Action
  from myrtle.agents.random_multi_action import RandomMultiAction
  An agent that will randomly select one or more actions at each time step, or none at all.

• Greedy, State-blind
  from myrtle.agents.greedy_state_blind import GreedyStateBlind
  An agent that will always select the action with the highest expected return.

• Greedy, State-blind, with epsilon exploration
  from myrtle.agents.greedy_state_blind_eps import GreedyStateBlindEpsilon
  An agent that will select the action with the highest expected return most of the time. The rest of the time it will select a single action at random.

Messaging

Communication between the Agent and the World is conducted through the Queues. Through the sensor_q the World passes sensor and reward information to the Agent. Through the action_q the Agent passes action commands back to the World.

The World also reports reward back to the parent bench process through a report_q Queue.

sensor_q messages

Roughly following the conventions of OpenAI Gym, messages through the sensor_q are dicts with one or more of the following key-value pairs.

• "sensors": numpy.Array, the values of all sensors.
• "rewards": List, the values of each reward. Some or all of them may be None.
• "truncated": bool, flag that the current episode has ended and another is being kicked off.
• "terminated": bool, flag that all episodes have ended, and no more sensor_q messages will be sent.

action_q messages

Messages through the action_q are dicts with a single key-value pair.

• "actions": numpy.Array, the values of all commanded actions.

report_q messages

Messages through the report_q are dicts with a three key-value pairs.

-"episode": int, the count of the current episode. -"step": int, the count of the current time step. Resets with each episode. -"rewards": List, as described for the sensor_q above.

Multiprocess coordination

One bit of weirdness about having the World and Agent running in separate processes is how to handle flow control. The World will be tied to the wall clock, advancing on a fixed cadence. It will keep providing sensor and reward information without regard for whether the Agent is ready for it. It will keep trying to do the next thing, regardless of whether the Agent has had enough time to decide what action to supply. The World does not accommodate the Agent. The responsibility for keeping up falls entirely on the Agent.

This means that the Agent must be able to handle the case where the World has provided multiple sensor/reward updates since the previous iteration. It also means that the World must be prepared to have one, zero, or multiple action commands from the Agent.

Saving and reporting results

If the benchmark is run with argument record=True (the default) then, the total reward for every time step reported by the World is written to a SQLite database, stored locally in a database file called bench.db.

Reporting and visualization scripts can be written that pull from these results.