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example_mountain_car: update the code to the new Gymnasium API #378

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example_mountain_car: update the code to the new Gymnasium API #378

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53 changes: 28 additions & 25 deletions examples/example_mountain_car.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@
import cgp

try:
import gym
import gymnasium as gym
except ImportError:
raise ImportError(
"Failed to import the OpenAI Gym package. Please install it via `pip install gym`."
Expand Down Expand Up @@ -68,29 +68,22 @@ class ConstantFloatTen(cgp.ConstantFloat):


def inner_objective(f, seed, n_runs_per_individual, n_total_steps, *, render):

env = gym.make("MountainCarContinuous-v0")

env.seed(seed)
env = gym.make("MountainCarContinuous-v0", render_mode="human" if render else None)

cum_reward_all_episodes = []
for _ in range(n_runs_per_individual):
observation = env.reset()
observation, _ = env.reset(seed=seed)

cum_reward_this_episode = 0
for _ in range(n_total_steps):

if render:
env.render()

continuous_action = f(*observation)
observation, reward, done, _ = env.step([continuous_action])
observation, reward, terminated, truncated, _ = env.step([continuous_action])
cum_reward_this_episode += reward

if done:
if terminated or truncated:
cum_reward_all_episodes.append(cum_reward_this_episode)
cum_reward_this_episode = 0
observation = env.reset()
observation, _ = env.reset(seed=seed)

env.close()

Expand Down Expand Up @@ -147,8 +140,14 @@ def evolve(seed):

objective_params = {"n_runs_per_individual": 3, "n_total_steps": 2000}

population_params = {"n_parents": 1, "seed": seed}

genome_params = {
"n_inputs": 2,
"n_outputs": 1,
"n_columns": 16,
"n_rows": 1,
"levels_back": None,
"primitives": (
cgp.Add,
cgp.Sub,
Expand All @@ -160,14 +159,19 @@ def evolve(seed):
),
}

ea_params = {"n_processes": 4}
ea_params = {
"n_offsprings": 4,
"tournament_size": 1,
"mutation_rate": 0.04,
"n_processes": 4
}

evolve_params = {
"max_generations": int(args["--max-generations"]),
"termination_fitness": 100.0,
}

pop = cgp.Population(genome_params=genome_params)
pop = cgp.Population(**population_params, genome_params=genome_params)

ea = cgp.ea.MuPlusLambda(**ea_params)

Expand All @@ -186,8 +190,8 @@ def recording_callback(pop):
n_total_steps=objective_params["n_total_steps"],
)

pop = cgp.evolve(
obj, pop, ea, **evolve_params, print_progress=True, callback=recording_callback
cgp.evolve(
pop, obj, ea, **evolve_params, print_progress=True, callback=recording_callback
)

return history, pop.champion
Expand Down Expand Up @@ -217,9 +221,7 @@ def plot_fitness_over_generation_index(history):
def evaluate_champion(ind):

env = gym.make("MountainCarContinuous-v0")

env.seed(seed)
observation = env.reset()
observation, _ = env.reset(seed=seed)

f = ind.to_func()

Expand All @@ -228,13 +230,14 @@ def evaluate_champion(ind):
while len(cum_reward_all_episodes) < 100:

continuous_action = f(*observation)
observation, reward, done, _ = env.step([continuous_action])
observation, reward, terminated, truncated, _ = env.step([continuous_action])
cum_reward_this_episode += reward

if done:

if terminated or truncated:
cum_reward_all_episodes.append(cum_reward_this_episode)
cum_reward_this_episode = 0
observation = env.reset()
observation, _ = env.reset(seed=seed)

env.close()

Expand Down Expand Up @@ -272,8 +275,8 @@ def visualize_behaviour_for_evolutionary_jumps(seed, history, only_final_solutio
x_0, x_1 = sympy.symbols("x_0, x_1")
f_lambdify = sympy.lambdify([x_0, x_1], expr)

def f(x, v):
return f_lambdify(x, v)
def f(x, y):
return f_lambdify(x, y)

inner_objective(f, seed, n_runs_per_individual, n_total_steps, render=True)

Expand Down