temp_height, temp_gate).reshape(
1, len(temp_height) + len(temp_gate))
# Action
q_values = prof_x.ac_model.predict_on_batch(input_states)
# Policy
action = epsi_greedy(len(action_space), q_values,
epsilon_value[episode_tracker])
# Implement Action
gates.update_gates(action_space[action])
implement_action(gates.current_gate, con_ponds, NODES_LIS)
# Run step
swmm.run_step()
# Receive the reward
overflow = [swmm.get(pond_name, swmm.FLOODING, swmm.SI) for pond_name in downstream_ponds]
depth = [swmm.get(pond_name, swmm.DEPTH, swmm.SI) for pond_name in downstream_ponds]
outflow = [swmm.get(NODES_LIS[pond_name], swmm.FLOW, swmm.SI) for pond_name in downstream_ponds]
reward = reward_function(outflow, depth, overflow)
reward_sim.append(reward)
# Update replay memory
# Heights
temp_new_height = np.asarray(
[swmm.get(i, swmm.DEPTH, swmm.SI) for i in nodes_list])
# Gate Positions
temp_new_gate = np.asarray(gates.current_gate)
# Initializing SWMM
# ***********************************************************************
swmm.initialize(inp) # Step 1
# ***********************************************************************
# Step Running
# ***********************************************************************
# Main loop: finished when the simulation time is over.
while( not swmm.is_over() ):
# ----------------- Run step and retrieve simulation time -----------
time.append( swmm.get_time() )
swmm.run_step() # Step 2
# --------- Retrieve & modify information during simulation ---------
# Retrieve information about flow in C-5
f = swmm.get('C-5', swmm.FLOW, swmm.SI)
# Stores the information in the flow vector
flow.append(f)
# Retrieve information about volume in V-1
v = swmm.get('V-1', swmm.VOLUME, swmm.SI)
# Stores the information in the volume vector
vol.append(v)
# --------------------------- Control Actions ------------------------
# If the flow in C-5 is greater or equal than 2 m3/s the setting
# upstream of the link is completely closed, else it is completely