def _make_experiment(exp_id=1, path="./Results/Tmp/test_FiftyChain"):
"""
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:
domain = FiftyChain()
## 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,
learn_rate=0.1)
checks_per_policy = 3
max_steps = 50
num_policy_checks = 3
experiment = Experiment(**locals())
return experiment
def test_errs():
""" Ensure that we can call custom methods without error """
domain = FiftyChain()
# [[Test storeOptimalPolicy()]]
domain.storeOptimalPolicy()
# [[Test L_inf_distance_to_V_star]]
exp = _make_experiment(exp_id=1)
exp.run(visualize_steps=False,
visualize_learning=False,
visualize_performance=0)
distToVStar = exp.domain.L_inf_distance_to_V_star(exp.agent.representation)
assert distToVStar is not np.NAN # must use `is not` because of np.NaN vs np.NAN vs...
assert distToVStar is not np.Inf
def test_errs():
""" Ensure that we can call custom methods without error """
domain = FiftyChain()
# [[Test storeOptimalPolicy()]]
domain.storeOptimalPolicy()
# [[Test L_inf_distance_to_V_star]]
exp = _make_experiment(exp_id=1)
exp.run(visualize_steps=False,
visualize_learning=False,
visualize_performance=0)
distToVStar = exp.domain.L_inf_distance_to_V_star(exp.agent.representation)
assert distToVStar is not np.NAN # must use `is not` because of np.NaN vs np.NAN vs...
assert distToVStar is not np.Inf
def test_transitions():
"""
Ensure that actions result in expected state transition behavior.
Note that if the agent attempts to leave the edge
(select LEFT from s0 or RIGHT from s49) then the state should not change.
NOTE: assume p_action_failure is only noise term.
"""
# [[initialize domain]]
domain = FiftyChain()
domain.p_action_failure = 0.0 # eliminate stochasticity
dummyS = domain.s0()
domain.state = 2 # state s2
left = domain.LEFT
right = domain.RIGHT
goals = domain.GOAL_STATES
# Check basic step
r, ns, terminal, possibleA = domain.step(left)
assert ns == 1 and terminal == False
assert np.all(possibleA == np.array([left,
right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
# Check another basic step
r, ns, terminal, possibleA = domain.step(left)
assert ns == 0 and terminal == False
assert np.all(possibleA == np.array([left,
right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
# Ensure state does not change or wrap around and that all actions
# remain availableon corner case, per domain spec
r, ns, terminal, possibleA = domain.step(left)
assert ns == 0 and terminal == False
assert np.all(possibleA == np.array([left,
right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
# A final basic step
r, ns, terminal, possibleA = domain.step(right)
assert ns == 1 and terminal == False
assert np.all(possibleA == np.array([left,
right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
def test_transitions():
"""
Ensure that actions result in expected state transition behavior.
Note that if the agent attempts to leave the edge
(select LEFT from s0 or RIGHT from s49) then the state should not change.
NOTE: assume p_action_failure is only noise term.
"""
# [[initialize domain]]
domain = FiftyChain()
domain.p_action_failure = 0.0 # eliminate stochasticity
dummyS = domain.s0()
domain.state = 2 # state s2
left = domain.LEFT
right = domain.RIGHT
goals = domain.GOAL_STATES
# Check basic step
r,ns,terminal,possibleA = domain.step(left)
assert ns == 1 and terminal == False
assert np.all(possibleA == np.array([left, right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
# Check another basic step
r,ns,terminal,possibleA = domain.step(left)
assert ns == 0 and terminal == False
assert np.all(possibleA == np.array([left, right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
# Ensure state does not change or wrap around and that all actions
# remain availableon corner case, per domain spec
r,ns,terminal,possibleA = domain.step(left)
assert ns == 0 and terminal == False
assert np.all(possibleA == np.array([left, right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
# A final basic step
r,ns,terminal,possibleA = domain.step(right)
assert ns == 1 and terminal == False
assert np.all(possibleA == np.array([left, right])) # all actions available
if ns in goals: assert r > 0
else: assert r <= 0
def makeComponents(self):
domain = FiftyChain()
representation = RepresentationFactory.get(
config=self.representationConfig,
name=str(self.lstRepresentation.currentItem().text()),
domain=domain)
policy = PolicyFactory.get(config=self.policyConfig,
name=str(
self.lstPolicy.currentItem().text()),
representation=representation)
agent = AgentFactory.get(config=self.agentConfig,
name=str(self.lstAgent.currentItem().text()),
representation=representation,
policy=policy)
return domain, agent
