refactored deep reinforcement learning example into train and test sc…

example_solutions/deep_reinforcement_learning/evaluate.py

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+# Plot the training process
+import pandas as pd
+from stable_baselines3 import SAC
+from stable_baselines3.common.results_plotter import plot_results
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+
+from building_energy_storage_simulation import BuildingSimulation, Environment
+from example_solutions.deep_reinforcement_learning.train import RESULT_PATH, NUM_FORECAST_STEPS
+from example_solutions.helper import read_data, TEST_INDEX_START, TEST_INDEX_END, BATTERY_POWER, BATTERY_CAPACITY, \
+    plot_control_trajectory
+from example_solutions.observation_wrapper import ObservationWrapper
+
+
+def evaluate(env, agent=None):
+    # Do the evaluation
+    actions, observations, electricity_consumption, price, rewards = ([], [], [], [], [])
+    done = False
+    obs = env.reset()
+    while not done:
+        if agent is None:
+            action = [[0]]
+        else:
+            action = [agent.predict(obs, deterministic=True)[0][0]]
+
+        obs, r, done, info = env.step([action[0][0]])
+
+        actions.append(action[0][0])
+        original_obs = env.get_original_obs()[0]
+        observations.append(original_obs)
+        electricity_consumption.append(info[0]['electricity_consumption'])
+        price.append(info[0]['electricity_price'])
+        rewards.append(r)
+
+    return pd.DataFrame({
+        'action': actions,
+        'observations': observations,
+        'electricity_consumption': electricity_consumption,
+        'electricity_price': price,
+        'reward': rewards
+    })
+
+
+if __name__ == "__main__":
+    # Plot evolution of reward during training
+    try:
+        plot_results(RESULT_PATH, x_axis='timesteps', task_name='title', num_timesteps=None)
+    except:
+        print('Training Reward Plot could not be created')
+
+    load, price, generation = read_data()
+    load_eval = load[TEST_INDEX_START:]
+    price_eval = price[TEST_INDEX_START:]
+    generation_eval = generation[TEST_INDEX_START:]
+
+    num_eval_timesteps = TEST_INDEX_END - TEST_INDEX_START
+
+    eval_sim = BuildingSimulation(electricity_load_profile=load_eval,
+                                  solar_generation_profile=generation_eval,
+                                  electricity_price=price_eval,
+                                  max_battery_charge_per_timestep=BATTERY_POWER,
+                                  battery_capacity=BATTERY_CAPACITY)
+
+    eval_env = Environment(eval_sim, num_forecasting_steps=NUM_FORECAST_STEPS, max_timesteps=num_eval_timesteps)
+    eval_env = ObservationWrapper(eval_env, NUM_FORECAST_STEPS)
+    eval_env = DummyVecEnv([lambda: eval_env])
+    # It is important to load the environmental statistics here as we use a rolling mean calculation !
+    eval_env = VecNormalize.load(RESULT_PATH + 'env.pkl', eval_env)
+    eval_env.training = False
+
+    model = SAC.load(RESULT_PATH + 'model')
+
+    trajectory = evaluate(eval_env, model)
+    baseline_trajectory = evaluate(eval_env, None)
+
+    cost = sum(trajectory['electricity_price'] * trajectory['electricity_consumption'])
+    baseline_cost = sum(baseline_trajectory['electricity_price'] * baseline_trajectory['electricity_consumption'])
+
+    print('baseline cost: ' + str(baseline_cost))
+    print('cost: ' + str(cost))
+    print('savings in %: ' + str(1 - cost / baseline_cost))
+
+    observation_df = trajectory['observations'].apply(pd.Series)
+    augmented_load = observation_df[1] + trajectory['action'] * BATTERY_POWER
+    plot_control_trajectory(residual_load=observation_df[1],
+                            augmented_load=augmented_load,
+                            price=trajectory['electricity_price'],
+                            battery_power=trajectory['action'] * BATTERY_POWER)