def make_hillcar_problem(step_len,
n_steps,
damp,
jitter,
discount,
bounds,
cost_radius,
actions):
"""
Makes a hillcar problem
TODO: take in parameters
"""
(A,action_dim) = actions.shape
assert(action_dim == 1)
state_dim = 2
# Set up parameters for the DI problem
trans_params = utils.kwargify(
mass=1.0,
step=step_len,
num_steps=n_steps,
dampening=damp,
jitter=jitter)
trans_fn = HillcarTransitionFunction(
**trans_params)
#boundary = SaturationBoundary(bounds)
boundary = HillcarBoundary(bounds)
cost_state_fn = BallSetFn(np.zeros(2), cost_radius)
cost_fn = CostWrapper(cost_state_fn)
cost_fn.favored=np.array([0.0])
#If we see 100 leaking in, there is a problem with v saturation
#with the boundary containment.
oob_costs = np.array([0,0,0,0])
# otherwise we will pay a penalty at the boundary, but be 0 after.
gen_model = GenerativeModel(trans_fn,
boundary,
cost_fn,
state_dim,
action_dim,
oob_costs)
action_boundary = [(actions[0],actions[-1])]
problem = Problem(gen_model,
action_boundary,
discount)
return problem
def make_di_problem(step_len,
n_steps,
damp,
jitter,
discount,
bounds,
cost_radius,
actions):
"""
Makes a double integrator problem
TODO: take in parameters
"""
(A,action_dim) = actions.shape
assert(action_dim == 1)
assert(actions[0] == -actions[-1])
state_dim = 2
# Set up parameters for the DI problem
trans_params = utils.kwargify(step=step_len,
num_steps=n_steps,
dampening=damp,
control_jitter=jitter)
trans_fn = DoubleIntegratorTransitionFunction(
**trans_params)
#boundary = DoubleIntBoundary(bounds)
#boundary = SaturationBoundary(bounds)
boundary = TorusBoundary(bounds)
cost_state_fn = BallSetFn(np.zeros(2), cost_radius)
#cost_state_fn = TargetZoneFn(np.array([[-0.5,0.5],[-0.5,0.5]]))
cost_fn = CostWrapper(cost_state_fn)
oob_costs = np.array([100]*2*state_dim)
gen_model = GenerativeModel(trans_fn,
boundary,
cost_fn,
state_dim,
action_dim,
oob_costs)
action_boundary = [(actions[0],actions[-1])]
problem = Problem(gen_model,
action_boundary,
discount)
return problem
def f(g):
self._plugins.append(Plugin(name, keys, kwargify(g), kwargs))
return g # So the markers can be chained