update functions. Visualize, Interpretable and with evox

examples/with_evox/walker2d_evox.py

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+import jax
+import jax.numpy as jnp
+
+from evox import workflows, problems
+
+from tensorneat.common.evox_adaptors import EvoXAlgorithmAdaptor, TensorNEATMonitor
+from tensorneat.algorithm import NEAT
+from tensorneat.genome import DefaultGenome, BiasNode
+from tensorneat.common import ACT, AGG
+
+neat_algorithm = NEAT(
+    pop_size=1000,
+    species_size=20,
+    survival_threshold=0.1,
+    compatibility_threshold=1.0,
+    genome=DefaultGenome(
+        max_nodes=50,
+        max_conns=200,
+        num_inputs=17,
+        num_outputs=6,
+        init_hidden_layers=(),
+        node_gene=BiasNode(
+            activation_options=ACT.tanh,
+            aggregation_options=AGG.sum,
+        ),
+        output_transform=ACT.tanh,
+    ),
+)
+evox_algorithm = EvoXAlgorithmAdaptor(neat_algorithm)
+
+key = jax.random.PRNGKey(42)
+model_key, workflow_key = jax.random.split(key)
+
+monitor = TensorNEATMonitor(neat_algorithm, is_save=False)
+problem = problems.neuroevolution.Brax(
+    env_name="walker2d",
+    policy=evox_algorithm.forward,
+    max_episode_length=1000,
+    num_episodes=1,
+)
+
+
+def nan2inf(x):
+    return jnp.where(jnp.isnan(x), -jnp.inf, x)
+
+
+# create a workflow
+workflow = workflows.StdWorkflow(
+    algorithm=evox_algorithm,
+    problem=problem,
+    candidate_transforms=[jax.jit(jax.vmap(evox_algorithm.transform))],
+    fitness_transforms=[nan2inf],
+    monitors=[monitor],
+    opt_direction="max",
+)
+
+# init the workflow
+state = workflow.init(workflow_key)
+
+# enable multi devices
+state = workflow.enable_multi_devices(state)
+
+# run the workflow for 100 steps
+for i in range(100):
+    train_info, state = workflow.step(state)
+    monitor.show()