def _make_experiment(exp_id=1, path="./Results/Tmp/test_PST"):
"""
Each file specifying an experimental setup should contain a
make_experiment function which returns an instance of the Experiment
class with everything set up.
@param id: number used to seed the random number generators
@param path: output directory where logs and results are stored
"""
# Domain:
NUM_UAV = 3
domain = PST(NUM_UAV=NUM_UAV)
# Representation
# discretization only needed for continuous state spaces, discarded otherwise
representation = IncrementalTabular(domain)
# Policy
policy = eGreedy(representation, epsilon=0.1)
# Agent
agent = SARSA(
representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
)
checks_per_policy = 2
max_steps = 30
num_policy_checks = 2
experiment = Experiment(**locals())
return experiment
def test_sarsa_valfun_chain():
"""
Check if SARSA computes the value function of a simple Markov chain correctly.
This only tests value function estimation, only one action possible
"""
rep = MockRepresentation()
pol = eGreedy(rep)
agent = SARSA(pol, rep, 0.9, lambda_=0.0)
for i in range(1000):
if i % 4 == 3:
continue
agent.learn(
np.array([i % 4]),
[0],
0,
1.0,
np.array([(i + 1) % 4]),
[0],
0,
(i + 2) % 4 == 0,
)
V_true = np.array([2.71, 1.9, 1, 0])
np.testing.assert_allclose(rep.weight_vec, V_true)
def _make_experiment(domain,
exp_id=1,
path="./Results/Tmp/test_InfTrackCartPole"):
## Representation
# discretization only needed for continuous state spaces, discarded otherwise
representation = Tabular(domain)
## Policy
policy = eGreedy(representation, epsilon=0.2)
## Agent
agent = SARSA(
representation=representation,
policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
)
checks_per_policy = 3
max_steps = 50
num_policy_checks = 3
experiment = Experiment(**locals())
return experiment
def test_deepcopy():
rep = MockRepresentation()
pol = eGreedy(rep)
agent = SARSA(pol, rep, 0.9, lambda_=0.0)
copied_agent = copy.deepcopy(agent)
assert agent.lambda_ == copied_agent.lambda_
def tabular_sarsa(domain, discretization=20, lambda_=0.3):
tabular = Tabular(domain, discretization=discretization)
policy = eGreedy(tabular, epsilon=0.1)
return SARSA(policy, tabular, domain.discount_factor, lambda_=lambda_)