add more rl task in examples

pipeline_time.py

@@ -0,0 +1,111 @@
+from typing import Type
+
+import jax
+import time
+import numpy as np
+
+from algorithm import NEAT, HyperNEAT
+from config import Config
+from core import State, Algorithm, Problem
+
+
+class Pipeline:
+
+    def __init__(self, config: Config, algorithm: Algorithm, problem_type: Type[Problem]):
+        self.config = config
+        self.algorithm = algorithm
+        self.problem = problem_type(config.problem)
+
+        if isinstance(algorithm, NEAT):
+            assert config.neat.inputs == self.problem.input_shape[-1]
+
+        elif isinstance(algorithm, HyperNEAT):
+            assert config.hyperneat.inputs == self.problem.input_shape[-1]
+
+        else:
+            raise NotImplementedError
+
+        self.act_func = self.algorithm.act
+
+        for _ in range(len(self.problem.input_shape) - 1):
+            self.act_func = jax.vmap(self.act_func, in_axes=(None, 0, None))
+
+        self.best_genome = None
+        self.best_fitness = float('-inf')
+        self.generation_timestamp = None
+
+    def setup(self):
+        key = jax.random.PRNGKey(self.config.basic.seed)
+        algorithm_key, evaluate_key = jax.random.split(key, 2)
+        state = State()
+        state = self.algorithm.setup(algorithm_key, state)
+        return state.update(
+            evaluate_key=evaluate_key
+        )
+
+    def step(self, state):
+
+        key, sub_key = jax.random.split(state.evaluate_key)
+        keys = jax.random.split(key, self.config.basic.pop_size)
+
+        pop = self.algorithm.ask(state)
+
+        pop_transformed = jax.vmap(self.algorithm.transform, in_axes=(None, 0))(state, pop)
+
+        fitnesses = jax.vmap(self.problem.evaluate, in_axes=(0, None, None, 0))(keys, state, self.act_func,
+                                                                                pop_transformed)
+
+        state = self.algorithm.tell(state, fitnesses)
+
+        return state.update(evaluate_key=sub_key), fitnesses
+
+    def auto_run(self, ini_state):
+        state = ini_state
+        for _ in range(self.config.basic.generation_limit):
+
+            self.generation_timestamp = time.time()
+
+            previous_pop = self.algorithm.ask(state)
+
+            state, fitnesses = self.step(state)
+
+            fitnesses = jax.device_get(fitnesses)
+
+            self.analysis(state, previous_pop, fitnesses)
+
+            if max(fitnesses) >= self.config.basic.fitness_target:
+                print("Fitness limit reached!")
+                return state, self.best_genome
+
+        print("Generation limit reached!")
+        return state, self.best_genome
+
+    def analysis(self, state, pop, fitnesses):
+
+        max_f, min_f, mean_f, std_f = max(fitnesses), min(fitnesses), np.mean(fitnesses), np.std(fitnesses)
+
+        new_timestamp = time.time()
+
+        cost_time = new_timestamp - self.generation_timestamp
+
+        max_idx = np.argmax(fitnesses)
+        if fitnesses[max_idx] > self.best_fitness:
+            self.best_fitness = fitnesses[max_idx]
+            self.best_genome = pop[max_idx]
+
+        member_count = jax.device_get(state.species_info.member_count)
+        species_sizes = [int(i) for i in member_count if i > 0]
+
+        print(f"Generation: {state.generation}",
+              f"species: {len(species_sizes)}, {species_sizes}",
+              f"fitness: {max_f:.6f}, {min_f:.6f}, {mean_f:.6f}, {std_f:.6f}, Cost time: {cost_time * 1000:.6f}ms")
+
+    def show(self, state, genome):
+        transformed = self.algorithm.transform(state, genome)
+        self.problem.show(state.evaluate_key, state, self.act_func, transformed)
+
+    def pre_compile(self, state):
+        tic = time.time()
+        print("start compile")
+        self.step.lower(self, state).compile()
+        print(f"compile finished, cost time: {time.time() - tic}s")