def __init__(self, world, seed, base, add):
add = Value.get_float(add, world.act_shape)
base = Agent.build(base, AddValueWorld(world, add), seed)
def process_action(a):
return a + add.get()
super().__init__(base, process_action=process_action)
def __init__(self, world, seed, log_lr, n_steps, init):
init = Value.get_float(init, world.act_shape)
# Wrap the original world with a meta world of learning rates
world = GradAscentLRWorld(world, n_steps, init)
log_lr = Agent.build(log_lr, world, seed)
# Run a single trajectory on GradAscentLRWorld
value = world.final_value(log_lr)
value.setflags(write=False)
super().__init__(get_action=lambda _: value)
