# Develop a new agent (for your simulation)¶

In the previous tutorial, we implemented a world simulation called TripsWorld in which agents negotiated how to spend their holiday seasons. In this tutorial we will develop agents for this world and take it on a test-drive.

## Making a Random Agent for the Trips World¶

Our random agent, will just use a random negotiator for everything and will not keep track of the history of other agents. That is the complete code which is self explanatory this time.

class RandomPerson(Person):
def step(self):
# get IDs of all ogher agents from the AWI
agents = self.awi.agents
# request the maximum number of negotiations possible
for _ in range(self.awi.n_negs):
# for each negotiation, use a random subset of partners and a random negotiator
self.awi.request_negotiation(
partners=sample(agents, k=randint(1, len(agents) - 1)),
negotiator=RandomNegotiator(),
)

def init(self):
# we need no initialization
pass

def respond_to_negotiation_request(
self, initiator: str, partners: List[str], mechanism: AgentMechanismInterface
) -> Optional[Negotiator]:
# just us a random negotiator for everything
return RandomNegotiator()

## Testing the world¶

We can now start world simulations using our new world and agent

world = TripsWorld(n_steps=10, construct_graphs=True)
for i in range(5):
world.join(RandomPerson(name=f"a{i}"))
world.run_with_progress()
100%|██████████| 10/10 [00:06<00:00,  1.50it/s]

Let’s see what happened in this run. Firstly, how many negotiations were conducted over time. Our agents always conducted the maximum number of negotiations ($$8$$) and we had $$5$$ agents which means we expect $$40$$ negotiations at every step.

plt.plot(world.stats['n_negotiations'])
plt.xlabel('Simulation Step')
plt.ylabel('N. Negotiations')
plt.show()

Let’s start by seeing how long did each step take (note that stats access the stats as a Dict[str, List] but stats_df access the same data as a pandas dataframe.

def stats_df(world):
return pd.DataFrame(world.stats)

plt.bar(range(world.n_steps), stats_df(world)['step_time'])
plt.xlabel('Simulation Step')
plt.ylabel('Time (s)')
plt.show()

We can for example check the welfare (activity level) of this world (defined as the total contract sizes executed per step which in our case correspond to the total welfare)

plt.plot(world.stats['activity_level'])
plt.xlabel('Simulation Step')
plt.ylabel('Activitiy Level (\$)\nTotal Welfare')
plt.show()

We can see a picture of contracting in this world as follows:

plt.plot(world.stats['n_contracts_signed'], label='Signed Contracts')
plt.plot(world.stats['n_contracts_executed'], label='Executed Contracts')
plt.legend()
plt.xlabel('Simulation Step')
plt.ylabel('N. Contracts')
plt.show()

We can also check the breaches that happened

plt.plot(world.stats['breach_level'])
plt.xlabel('Simulation Step')
plt.ylabel('Total Breach Level')
plt.show()

We can actually check what happens to ALL agents

fig, utility = plt.subplots(1, 1)
snames = sorted(_.name for _ in world.agents.values())
for name in snames:
utility.plot(np.asarray(world.stats[f'total_utility_{name}']), label=name)
utility.set(xlabel='Simulation Step', ylabel='Player Total Utility')
utility.legend(loc='lower left')
fig.show()

As you can see, the total utility is not monotonically increasing. This means that agents accepted offers that have a utility less than their reserved value. That is expected because we use RandomNegotiators for all negotiations.

We can also get a graphical view of all activities during the simulation:

world.draw(steps=(0, world.n_steps), together=False, ncols=2, figsize=(20, 20))
plt.show()

## Making a Better Agent for the Trips World¶

As we have seen, the random agent did not behave well in this world. It is pretty simple to extend it into a better agent. Here is a simple attempt:

class SanePerson(Person):
"""An agent that uses a predefined negotiator instead of a random negotiator"""
def __init__(self, *args, negotiator_type = AspirationNegotiator, negotiator_params = None, **kwargs):
super().__init__(*args, **kwargs)
self.negotiator_type = negotiator_type
self.negotiator_params = negotiator_params if negotiator_params is not None else dict()

def step(self):
# get IDs of all ogher agents from the AWI
agents = self.awi.agents
# request the maximum number of negotiations possible
for _ in range(self.awi.n_negs):
# for each negotiation, use a random subset of partners and a random negotiator
self.awi.request_negotiation(
partners=sample(agents, k=randint(1, len(agents) - 1)),
negotiator=self.negotiator_type(ufun=self.ufun, **self.negotiator_params),
)

def init(self):
pass

def respond_to_negotiation_request(
self, initiator: str, partners: List[str], mechanism: AgentMechanismInterface
) -> Optional[Negotiator]:
# just us a random negotiator for everything
return self.negotiator_type(ufun=self.ufun, **self.negotiator_params)

The only difference between this SanePerson and the RandomPerson we developed earlier is that it can be constructed to use any type of negotiation strategy supported in NegMAS (i.e. any SAONegotiator class). Whenever it is asked for a negotiator (either in step or respond_to_negotiation_request) it uses that negotiator setting its utility function.

Let’s try it

world = TripsWorld(n_steps=40, construct_graphs=True)
for i in range(3):
world.join(RandomPerson(name=f"rand-{i}"))
for i in range(3):
world.join(SanePerson(name=f"sane-{i}"))
world.run_with_progress()
100%|██████████| 40/40 [01:27<00:00,  2.20s/it]

Let’s check how did our agent do

fig, utility = plt.subplots(1, 1)
snames = sorted(_.name for _ in world.agents.values())
utils = dict(sane=np.zeros(world.n_steps), rand=np.zeros(world.n_steps))
for agent_name in (_.name for _ in world.agents.values()):
utils[agent_name.split("-")[0]] += np.asarray(world.stats[f'total_utility_{agent_name}'])
for name in utils.keys():
utility.plot(utils[name], label=name)
utility.set(xlabel='Simulation Step', ylabel='Player Total Utility')
utility.legend(loc='lower left')
fig.show()

Better.