Merge branch 'multi_dimensional_action' into boptest-gym-service

examples/run_multiaction.py

@@ -0,0 +1,54 @@
+import random
+from stable_baselines3 import DQN
+from boptestGymEnv import BoptestGymEnv, NormalizedObservationWrapper, DiscretizedActionWrapper
+
+url = 'http://127.0.0.1:5000'
+seed = 123456
+
+# Seed for random starting times of episodes
+random.seed(seed)
+
+def train_multiaction():
+    '''Method to train a DQN agent with a multi-dimensional action environment. 
+
+    '''
+
+    env = BoptestGymEnv(
+            url=url,
+            actions=['oveTZonSet_u', 'oveTSupSet_u', 'oveCO2ZonSet_u'],
+            observations={
+                'time': (0, 604800),
+                'reaTZon_y': (280., 310.),
+                'TDryBul': (265, 303),
+                'HDirNor': (0, 862),
+                'InternalGainsRad[1]': (0, 219),
+                'PriceElectricPowerHighlyDynamic': (-0.4, 0.4),
+                'LowerSetp[1]': (280., 310.),
+                'UpperSetp[1]': (280., 310.)
+            },
+            predictive_period=24*3600,
+            regressive_period=6*3600,
+            max_episode_length=24*3600,
+            warmup_period=24*3600,
+            step_period=3600,
+            random_start_time=False,
+            start_time=31*24*3600
+        )
+
+    # Normalize observations and discretize action space
+    env = NormalizedObservationWrapper(env)
+    env = DiscretizedActionWrapper(env, n_bins_act=10)
+
+    # Instantiate an RL agent
+    model = DQN('MlpPolicy', env, verbose=1, gamma=0.99,
+                learning_rate=5e-4, batch_size=24, seed=seed,
+                buffer_size=365*24, learning_starts=24, train_freq=1)
+
+    model.learn(total_timesteps=100)
+
+    return env, model
+
+
+
+
+