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Fix _STEP and _RESET for TempVMPackingEnv (fixes #36) #42

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Fix _STEP and _RESET for TempVMPackingEnv (fixes #36) #42

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23 changes: 16 additions & 7 deletions or_gym/envs/classic_or/vmpacking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,7 +172,7 @@ def __init__(self, *args, **kwargs):
super().__init__()
self.state = self.reset()

def step(self, action):
def _STEP(self, action):
done = False
pm_state = self.state["state"][:-1]
demand = self.state["state"][-1, 1:]
Expand All @@ -198,7 +198,8 @@ def step(self, action):
# Remove process from PM
if self.durations[process] == self.current_step:
pm = self.assignment[process] # Find PM where process was assigned
pm_state[pm, self.load_idx] -= self.demand[process]
pm_state[pm, self.load_idx] -= self.demand[process][1:] # Index to exclude first element of demand array
pm_state[pm, self.load_idx] = np.where(pm_state[pm, self.load_idx]<self.tol, 0., pm_state[pm, self.load_idx]) # Address rounding
# Shut down PM's if state is 0
if pm_state[pm, self.load_idx].sum() == 0:
pm_state[pm, 0] = 0
Expand All @@ -212,21 +213,29 @@ def step(self, action):
def update_state(self, pm_state):
# Make action selection impossible if the PM would exceed capacity
step = self.current_step if self.current_step < self.step_limit else self.step_limit-1
data_center = np.vstack([pm_state, self.demand[step]])
data_center = np.vstack([pm_state, self.demand[step]], dtype=np.float32)
data_center = np.where(data_center>1,1,data_center) # Fix rounding errors
self.state["state"] = data_center
self.state["action_mask"] = np.ones(self.n_pms)
self.state["avail_actions"] = np.ones(self.n_pms)
self.state["action_mask"] = np.ones(self.n_pms, dtype=np.uint8)
self.state["avail_actions"] = np.ones(self.n_pms, dtype=np.uint8)
if self.mask:
action_mask = (pm_state[:, 1:] + self.demand[step, 1:]) <= 1
self.state["action_mask"] = (action_mask.sum(axis=1)==2).astype(int)
self.state["action_mask"] = (action_mask.sum(axis=1)==2).astype(np.uint8)

def _RESET(self):
self.current_step = 0
self.assignment = {}
self.demand = self.generate_demand()
self.durations = generate_durations(self.demand)
self.state = (np.zeros((self.n_pms, 3)), self.demand[0])

self.state = {
"action_mask": np.ones(self.n_pms, dtype=np.uint8),
"avail_actions": np.ones(self.n_pms, dtype=np.uint8),
"state": np.vstack([
np.zeros((self.n_pms, 3)),
self.demand[self.current_step]],
dtype=np.float32)
}
return self.state

def step(self, action):
Expand Down