update readme.md for environment configuration

README.md

@@ -23,12 +23,15 @@
 TensorNEAT is a JAX-based libaray for NeuroEvolution of Augmenting Topologies (NEAT) algorithms, focused on harnessing GPU acceleration to enhance the efficiency of evolving neural network structures for complex tasks. Its core mechanism involves the tensorization of network topologies, enabling parallel processing and significantly boosting computational speed and scalability by leveraging modern hardware accelerators. TensorNEAT is compatible with the [EvoX](https://github.com/EMI-Group/evox/) framewrok.
 
 ## Requirements
-TensorNEAT requires:
-- jax (version >= 0.4.16)
-- jaxlib (version >= 0.3.0)
-- brax [optional]
-- gymnax [optional]
+Due to the rapid iteration of JAX versions, configuring the runtime environment for tensorNEAT can be challenging. We recommend the following versions for the relevant libraries:
+
+- jax (0.4.28)
+- jaxlib (0.4.28+cuda12.cudnn89)
+- brax (0.10.3)
+- gymnax (0.0.8)
   
+We provide detailed JAX-related environment references in [recommend_environment](recommend_environment.txt). If you encounter any issues while configuring the environment yourself, you can use this as a reference.
+
 ## Example
 Simple Example for XOR problem:
 ```python

recommend_environment.txt

@@ -0,0 +1,9 @@
+brax==0.10.3
+flax==0.8.4
+gymnax==0.0.8
+jax==0.4.28
+jaxlib==0.4.28+cuda12.cudnn89
+jaxopt==0.8.3
+mujoco==3.1.4
+mujoco-mjx==3.1.4
+optax==0.2.2

tensorneat/examples/brax/walker.py

@@ -0,0 +1,36 @@
+from pipeline import Pipeline
+from algorithm.neat import *
+
+from problem.rl_env import BraxEnv
+from utils import Act
+
+if __name__ == '__main__':
+    pipeline = Pipeline(
+        algorithm=NEAT(
+            species=DefaultSpecies(
+                genome=DefaultGenome(
+                    num_inputs=17,
+                    num_outputs=6,
+                    max_nodes=50,
+                    max_conns=100,
+                    node_gene=DefaultNodeGene(
+                        activation_options=(Act.tanh,),
+                        activation_default=Act.tanh,
+                    )
+                ),
+                pop_size=10,
+                species_size=10,
+            ),
+        ),
+        problem=BraxEnv(
+            env_name='walker2d',
+        ),
+        generation_limit=10000,
+        fitness_target=5000
+    )
+
+    # initialize state
+    state = pipeline.setup()
+    # print(state)
+    # run until terminate
+    state, best = pipeline.auto_run(state)