TODO: separate this content into a blog post and the github repository

The challenge is fusing them all togethor:

• We already have reasonably well performing modality-specific architectures (VGG, BERT, WaveNet), but no general vision, language, audio architecture.
• We have foundation models (gpt3, ViT), but where are the foundation policies?
• What’s the most efficient way to shuttle information from dataset to model?

I prpose developing a modular system to address these challanges: the Multi Agent Network (MAN). The following is a declarative description of what I intend to make:

The Multi Agent Network is an iterative update deep learning architecture composed of a network of agents that read and write data to variables (like salina). Variables have an owner (agent name), local name, type, value, and gradient (optional).

Learning: Learning can be local to the agent:

• connection formation/growth/prunning
• agent binary fision and agent death
• agent internal parameter updates Learning can also be coordinated by variables with a reward type.The man library includes a compute energy estimation function.

Optional Computation: No signal – even input signals – except energy is required to be present. The MAN is a sparse self-gated network of experts and agent outputs are not applied to their output variables if None.

Agents may be specialized or common. Common agents share a sparse activation distribution which facilitates open-ended growth. Common agents include:

• PredAgent
• SORNAgent
• SOMPAgent

Specialized agents represent a (part of a) specific ML model. Their ports must be specifically labeled corresponding to the expected flow of information. man includes:

• man.agents.specialized.bert
• man.agents.specialized.gpt3
• man.agents.specialized.vit
• man.agents.specialized.wavenet
• man.agents.specialized.xlm
• man.agents.specialized.triple_graph can be initialized empty or with DBPedia, Wikidata, other queryable knowledge graphs.
• man.agents.specialized.gym_interface provides a source of energy via reward. Also the standard input and output
• man.agents.specialized.dataset_interface
• man.agents.specialized.video
• man.agents.specialized.audio
• man.agents.specialized.webcam

From the developer perspective, it should be easy to do things like

man = MAN(interface=dict(obs=env.obs_spec, act=env.action_spec))
# itegrate with common RL loops
traj = executor.run(man, env)
trainier.train(man, env, traj)

# itergrate with ML pipelines
man = MAN(example=ds[0])
man.fit(ds, epochs=10)
man.evaluate(ds)

# make cool MAN's quickly
man = MAN(agents=dict(
bert=man.agents.specialized.bert,
gpt3=man.agents.specialized.gpt3,
reward=man.agents.specialized.gym_interface(myenv),
webcam=man.agents.specialized.webcam,
somp0=man.agents.common.somp.SOMP(),
somp1=man.agents.common.somp.SOMP(),
somp2=man.agents.common.somp.SOMP(),
somp3=man.agents.common.somp.SOMP(),
compute_energy=man.agents.common.compute_energy.ComputeEnergyEstimator(),
), connections=[
('gpt3:input', 'somp1:input'),
...
], grow=True)

state = man.reset()
for _ in range(10):
state = man.step(state)
man.visualize_state(state)

# add a new modality to the man

man.save('my_man.pkl')
man.show_graph()
man.save_model('my_model.savedmodel')  # may not allow for future growth. Just a compiled single iteration cell.



I need to think about all of this. Here’s an idea


workspace = Workspace()
workspace['x'].set(val)
workspace['name.val'].set(val)

agent(workspace)


What should I add to salina?

1. allow inspecting the types of the variables in the workspace This can already be done with a Workspace
2. make it easy to inspect and change the multiagent communication topology (for common agents to grow and prune neighbors) This can be done by defining register_child and register_parent functions for the CommonAgent base class. Children shouldn’t be agnostic to which parent is sending set points on their top outputs anyway.
class CommonAgent(salina.Agent):
def register_child(self, child):
self.children.append(child)
def register_parent(self, parent):
self.parents.append(parent)

1. provide a convenience initializer to define common agent topologies Then you combine the common agents with specialized agents.
common_agents = CommonAgents(
agents=[
my_common_agent_1,
SOMP(name='somp0'),
SORN(name='sorn0'),
...
],
connections=[
('somp0:top', 'somp1:bottom'),
(my_common_agent_1.ports.top, 'sorn0')
...
],
)


## New Attempt

A Multi Agent Network MAN includes

• a salina workspace to store data. allows using salina conventions.
• a list of agents: to track the agents, update them in order, and add/remove ones. users can reach in an wrap a salina Agents object around them.
• a timestep variable defined on the workspace: to track the current timestep.
• a __call__ function that takes any number of keyword arguments and overrides the workspace variables with those values, performs N_steps steps, and returns the return_var_keys workspace variables for the given timestep. This allows treating a MAN like a regular function and running it through an ML pipeline, env-agent executor, or another MAN.
• a start function which runs the workspace until the stop function is called (by an agent internally or asynchronously).
• at least one CommonAgent in self.agents: Common Agents
• namespace their variables with their name
• have a device which biases the set of neighbors they gravitate towards connecting with
• keep track of parents and children internally (see number 2 above)
• share a sparse representation language where 0 is equivalent to None
• share a common variable interpretation for reward, TODO
• reproduce (split into two commonagents), die (autopoeisis), and mutate (train)
• at least one RewardAgent in self.agents to distribute credit to other reward agents and common agents.
• any number of other agents in self.agents: such as internal datasets, replay buffers, state estimators, triple graph query agents, etc.

CommonAgent’s

## New Attempt

The Multi Agent Network (MAN) extends salina with an executor and common agents.

An executor

• contains a workspace and agents
• helps you schedule updates on particular subsets of agents
• indefinitely
• alternating periodic
• update condition for each agent
• provides a feedforeward wrapper to update the workspace variable for the current timestep

Common agents

• keep track of, connect, and prune their lateral neighbors, parents, and children
• reproduce, train, and die
• share a sparse representation language where 0 is equivalent to None
• share a common dataflow interpretation for bottom, side, and top (not all agents use all these variables)
• namescope their variables and only operate on other name-scoped variables

The executor also passes itself into the call function for agents that are common agents. The common agent can then look at All of the agents in the executors agent pool to see if it wants to form parent-child connections. The agent makes parent child connections by calling the dot at parent function on its child and calling the dot add child function on itself

You have the search for your own parents and children parentname:top > selfname:side? > selfname:top childname:bottomgrad:selfname > self:bottomgrad:parentname