<?phpuse Illuminate\Support\Facades\Route;
use App\Http\Controllers\UploadController;
/*|--------------------------------------------------------------------------| Web Routes|--------------------------------------------------------------------------|| Here is where you can register web routes for your application. These| routes are loaded by the RouteServiceProvider within a group which| contains the "web" middleware group. Now create something great!|*/
Route::get('/', function(){return view('welcome');
});
Route::get('/upload', [UploadController::class, 'index']);
Route::post('/upload', [UploadController::class, 'upload']);
<?phpuse Illuminate\Support\Facades\Route;
use App\Http\Controllers\AiController;
/*|--------------------------------------------------------------------------| Web Routes|--------------------------------------------------------------------------|| Here is where you can register web routes for your application. These| routes are loaded by the RouteServiceProvider within a group which| contains the "web" middleware group. Now create something great!|*/
Route::get('/', function(){return view('welcome');
});
Route::get('/ai', [AiController::class, 'index']);
import numpy as np
import gym
from gym_robot_arm.envs.robot_arm_env import RobotArmEnvV0
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
ENV_NAME = 'robot-arm-v0'# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in tensorflow.keras optimizer and# even the metrics!
memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
target_model_update=1e-2, policy=policy)
dqn.compile(Adam(learning_rate=1e-3), metrics=['mae'])
# Okay, now it's time to learn something! We visualize the training here for show, but this# slows down training quite a lot. You can always safely abort the training prematurely using# Ctrl + C.
dqn.fit(env, nb_steps=50000, visualize=True, verbose=2)
# After training is done, we save the final weights.
dqn.save_weights(f'./models/dqn_{ENV_NAME}_weights.h5f', overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env, nb_episodes=5, visualize=True)
import numpy as np
import gym
from gym_robot_arm.envs.robot_arm_env import RobotArmEnvV0
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
ENV_NAME = 'robot-arm-v0'# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in tensorflow.keras optimizer and# even the metrics!
memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
target_model_update=1e-2, policy=policy)
dqn.compile(Adam(learning_rate=1e-3), metrics=['mae'])
# After training is done, we save the final weights.
dqn.load_weights(f'./models/dqn_{ENV_NAME}_weights.h5f')
# Finally, evaluate our algorithm for 500 episodes.
dqn.test(env, nb_episodes=500, visualize=True) #500回の動作テスト
