delete useless;

append readme

README.md

@@ -1,3 +1,88 @@
-# TensorNEAT: Tensorized NeuroEvolution of Augmenting Topologies for GPU Acceleration
+# TensorNEAT: Tensorized NEAT implementation in JAX
 
-TensorNEAT is a powerful tool that utilizes JAX to implement the NEAT (NeuroEvolution of Augmenting Topologies) algorithm. It provides support for parallel execution of tasks such as forward network computation, mutation, and crossover at the population level.
+TensorNEAT is a powerful tool that utilizes JAX to implement the NEAT (NeuroEvolution of Augmenting Topologies) 
+algorithm. It provides support for parallel execution of tasks such as network forward computation, mutation, 
+and crossover at the population level.
+
+## Requirements
+* available [JAX](https://github.com/google/jax#installation) environment;
+* [gymnax](https://github.com/RobertTLange/gymnax) (optional).
+
+## Example
+Simple Example for XOR problem:
+```python
+from config import *
+from pipeline import Pipeline
+from algorithm import NEAT
+from algorithm.neat.gene import NormalGene, NormalGeneConfig
+from problem.func_fit import XOR, FuncFitConfig
+
+if __name__ == '__main__':
+    # running config
+    config = Config(
+        basic=BasicConfig(
+            seed=42,
+            fitness_target=-1e-2,
+            pop_size=10000
+        ),
+        neat=NeatConfig(
+            inputs=2,
+            outputs=1
+        ),
+        gene=NormalGeneConfig(),
+        problem=FuncFitConfig(
+            error_method='rmse'
+        )
+    )
+    # define algorithm: NEAT with NormalGene
+    algorithm = NEAT(config, NormalGene)
+    # full pipeline
+    pipeline = Pipeline(config, algorithm, XOR)
+    # initialize state
+    state = pipeline.setup()
+    # run until terminate
+    state, best = pipeline.auto_run(state)
+    # show result
+    pipeline.show(state, best)
+```
+
+Simple Example for RL envs in gymnax(CartPole-v0):
+```python
+import jax.numpy as jnp
+
+from config import *
+from pipeline import Pipeline
+from algorithm import NEAT
+from algorithm.neat.gene import NormalGene, NormalGeneConfig
+from problem.rl_env import GymNaxConfig, GymNaxEnv
+
+if __name__ == '__main__':
+    conf = Config(
+        basic=BasicConfig(
+            seed=42,
+            fitness_target=500,
+            pop_size=10000
+        ),
+        neat=NeatConfig(
+            inputs=4,
+            outputs=1,
+        ),
+        gene=NormalGeneConfig(
+            activation_default=Act.sigmoid,
+            activation_options=(Act.sigmoid,),
+        ),
+        problem=GymNaxConfig(
+            env_name='CartPole-v1',
+            output_transform=lambda out: jnp.where(out[0] > 0.5, 1, 0)  # the action of cartpole is {0, 1}
+        )
+    )
+
+    algorithm = NEAT(conf, NormalGene)
+    pipeline = Pipeline(conf, algorithm, GymNaxEnv)
+    state = pipeline.setup()
+    state, best = pipeline.auto_run(state)
+```
+
+`/examples` folder contains more examples.
+
+## TO BE COMPLETE...