add aaai exp

aaai_exp.py

@@ -21,16 +21,16 @@ def conf_cartpole():
             pop_size=10000
         ),
         neat=NeatConfig(
-            inputs=4,
+            inputs=3,
-            outputs=2,
+            outputs=1,
         ),
         gene=NormalGeneConfig(
             activation_default=Act.tanh,
             activation_options=(Act.tanh,),
         ),
         problem=GymNaxConfig(
-            env_name='CartPole-v1',
+            env_name='Pendulum-v1',
-            output_transform=lambda out: jnp.argmax(out)  # the action of cartpole is {0, 1}
+            output_transform=lambda out: out * 2  # the action of pendulum is [-2, 2]
         )
     )
 
@@ -100,6 +100,9 @@ def main():
     alg_state = algorithm.setup(alg_key)
 
     for i in range(conf.basic.generation_limit):
+
+        total_tic = time()
+
         pro_key, _ = jax.random.split(pro_key)
 
         fitnesses, a1, env_time, forward_time = batch_evaluate(
@@ -118,9 +121,10 @@ def main():
         a2 = time() - alg_tic
 
         alg_time = a1 + a2
+        total_time = time() - total_tic
 
-        print(f"generation:{i}, alg_time: {alg_time:.2f}, env_time: {env_time:.2f}, forward_time: {forward_time:.2f}, "
+        print(f"generation: {i}, alg_time: {alg_time:.2f}, env_time: {env_time:.2f}, forward_time: {forward_time:.2f}, total_time: {total_time: .2f}, "
-              f"max_fitness: {np.max(fitnesses):.2f}")
+              f"max_fitness: {np.max(fitnesses):.2f}", f"avg_fitness: {np.mean(fitnesses):.2f}")
 
 
 if __name__ == '__main__':

examples/a.py

@@ -0,0 +1,33 @@
+import jax.random
+import numpy as np
+import jax.numpy as jnp
+import time
+
+
+def random_array(key):
+    return jax.random.normal(key, (1000,))
+
+def random_array_np():
+    return np.random.normal(size=(1000,))
+
+
+def t_jax():
+    key = jax.random.PRNGKey(42)
+    max_li = []
+    tic = time.time()
+    for _ in range(100):
+        key, sub_key = jax.random.split(key)
+        array = random_array(sub_key)
+        array = jax.device_get(array)
+        max_li.append(max(array))
+    print(max_li, time.time() - tic)
+
+def t_np():
+    max_li = []
+    tic = time.time()
+    for _ in range(100):
+        max_li.append(max(random_array_np()))
+    print(max_li, time.time() - tic)
+
+if __name__ == '__main__':
+    t_np()