Add cartpole
This commit is contained in:
František Kmječ
parent
3d580c8ef2
commit
d832df4337
131
gym_cartpole.py
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131
gym_cartpole.py
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#!/usr/bin/env python3
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import argparse
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import datetime
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import os
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import re
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from typing import Optional
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os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "2") # Report only TF errors by default
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import numpy as np
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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# Credits to Milan Straka for making this task;
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# github repo github.com/ufal/npfl129
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# Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
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def evaluate_model(
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model: tf.keras.Model, seed: int = 42, episodes: int = 100, render: bool = False, report_per_episode: bool = False
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) -> float:
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"""Evaluate the given model on CartPole-v1 environment.
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Returns the average score achieved on the given number of episodes.
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"""
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import gym
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# Create the environment
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env = gym.make("CartPole-v1")
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env.seed(seed)
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# Evaluate the episodes
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total_score = 0
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for episode in range(episodes):
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observation, score, done = env.reset(), 0, False
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while not done:
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if render:
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env.render()
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prediction = model(observation[np.newaxis, ...])[0].numpy()
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if len(prediction) == 1:
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action = 1 if prediction[0] > 0.5 else 0
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elif len(prediction) == 2:
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action = np.argmax(prediction)
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else:
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raise ValueError("Unknown model output shape, only 1 or 2 outputs are supported")
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observation, reward, done, info = env.step(action)
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score += reward
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total_score += score
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if report_per_episode:
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print("The episode {} finished with score {}.".format(episode + 1, score))
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return total_score / episodes
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parser = argparse.ArgumentParser()
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# These arguments will be set appropriately by ReCodEx, even if you change them.
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parser.add_argument("--evaluate", default=False, action="store_true", help="Evaluate the given model")
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parser.add_argument("--recodex", default=False, action="store_true", help="Evaluation in ReCodEx.")
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parser.add_argument("--render", default=False, action="store_true", help="Render during evaluation")
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parser.add_argument("--seed", default=42, type=int, help="Random seed.")
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parser.add_argument("--threads", default=1, type=int, help="Maximum number of threads to use.")
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# If you add more arguments, ReCodEx will keep them with your default values.
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parser.add_argument("--batch_size", default=10, type=int, help="Batch size.")
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parser.add_argument("--epochs", default=100, type=int, help="Number of epochs.")
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parser.add_argument("--model", default="gym_cartpole_model.h5", type=str, help="Output model path.")
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parser.add_argument("--hidden_layer", default=200, type=int, help="Size of the hidden layer.")
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def main(args: argparse.Namespace) -> Optional[tf.keras.Model]:
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# Fix random seeds and threads
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np.random.seed(args.seed)
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tf.random.set_seed(args.seed)
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tf.config.threading.set_inter_op_parallelism_threads(args.threads)
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tf.config.threading.set_intra_op_parallelism_threads(args.threads)
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if not args.evaluate:
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# Create logdir name
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args.logdir = os.path.join("logs", "{}-{}-{}".format(
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os.path.basename(globals().get("__file__", "notebook")),
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datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S"),
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",".join(("{}={}".format(re.sub("(.)[^_]*_?", r"\1", k), v) for k, v in sorted(vars(args).items())))
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))
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# Load the data
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data = np.loadtxt("gym_cartpole_data.txt")
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observations, labels = data[:, :-1], data[:, -1].astype(np.int32)
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train_observations, test_observations, train_labels, test_labels = train_test_split(
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observations, labels, test_size=0.2, random_state=args.seed, stratify=labels)
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# TODO: Create the model in the `model` variable. Note that
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# the model can perform any of:
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# - binary classification with 1 output and sigmoid activation;
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# - two-class classification with 2 outputs and softmax activation.
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FEATURES = 4
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model = tf.keras.Sequential()
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model.add(tf.keras.layers.Input([FEATURES]))
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model.add(tf.keras.layers.Dense(args.hidden_layer, activation=tf.nn.relu))
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model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))
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# TODO: Prepare the model for training using the `model.compile` method.
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model.compile(
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optimizer=tf.optimizers.Adam(),
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loss=tf.losses.BinaryCrossentropy(),
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metrics=[tf.metrics.BinaryAccuracy("accuracy")]
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)
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tb_callback = tf.keras.callbacks.TensorBoard(args.logdir, histogram_freq=1)
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model.fit(
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train_observations, train_labels,
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validation_data=(test_observations, test_labels),
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batch_size=args.batch_size, epochs=args.epochs,
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callbacks=[tb_callback]
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)
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# Save the model, without the optimizer state.
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model.save(args.model, include_optimizer=False)
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else:
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# Evaluating, either manually or in ReCodEx
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model = tf.keras.models.load_model(args.model, compile=False)
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if args.recodex:
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return model
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else:
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score = evaluate_model(model, seed=args.seed, render=args.render, report_per_episode=True)
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print("The average score was {}.".format(score))
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if __name__ == "__main__":
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args = parser.parse_args([] if "__file__" not in globals() else None)
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main(args)
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100
gym_cartpole_data.txt
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100
gym_cartpole_data.txt
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-0.310 0.322 -0.022 0.042 0
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-0.505 -0.150 -0.022 -0.338 1
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-0.160 -0.738 0.080 0.254 1
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-0.471 -0.052 -0.000 0.140 0
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-0.918 -0.703 0.058 0.073 0
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1.336 -0.546 -0.063 0.391 1
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-1.175 0.138 0.162 -0.084 0
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-1.465 -0.183 0.033 0.006 0
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-0.282 -0.401 -0.010 0.323 0
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1.729 -0.590 0.017 0.325 1
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0.498 -0.004 -0.034 -0.081 0
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-0.830 -0.515 0.080 0.109 1
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0.000 0.043 0.021 -0.082 1
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0.209 -0.693 -0.038 0.221 0
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0.153 0.210 -0.025 -0.021 1
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0.278 0.786 -0.041 -0.400 0
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-0.169 -0.502 0.043 0.029 1
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0.751 0.010 -0.109 0.052 0
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0.053 0.372 -0.041 -0.145 0
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-0.367 0.606 0.026 -0.597 0
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0.317 -0.574 -0.098 0.292 0
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0.656 0.679 -0.026 -0.067 0
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-0.047 0.591 -0.122 -0.339 0
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-0.069 0.521 -0.021 -0.007 1
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-0.485 -0.369 -0.085 0.215 1
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-0.216 -0.918 -0.007 0.509 1
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-0.149 0.350 0.016 -0.158 0
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-0.072 -0.219 -0.066 -0.182 1
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-0.512 -0.326 -0.041 -0.002 1
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0.050 0.194 0.071 0.037 1
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0.025 0.179 -0.133 0.087 0
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-0.290 1.088 -0.146 -0.438 0
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-0.308 -0.713 -0.020 0.329 1
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-0.313 -0.347 0.004 0.067 0
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0.138 -0.063 -0.073 0.352 1
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-0.172 0.895 0.002 -0.488 0
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-0.068 -0.787 -0.052 0.282 0
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-0.296 -0.025 -0.015 -0.263 0
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-0.767 -0.355 -0.047 0.292 1
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0.422 0.720 -0.051 -0.218 1
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-1.656 0.217 0.038 -0.340 0
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-0.305 -0.010 -0.062 -0.104 0
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-0.972 0.426 0.069 -0.558 0
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-0.932 0.146 -0.043 0.305 1
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0.428 -0.738 -0.007 0.329 1
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0.727 -0.382 -0.026 0.370 0
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0.634 0.020 0.061 0.185 0
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0.532 0.573 -0.009 -0.593 0
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-0.511 0.188 -0.038 -0.050 1
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0.497 -0.555 -0.072 0.174 1
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0.035 -0.032 0.023 0.197 1
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-0.559 -0.136 0.033 -0.007 1
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0.369 0.543 -0.065 -0.003 1
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0.826 -0.175 0.018 -0.069 0
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-0.734 -0.697 0.076 -0.004 0
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-0.494 0.165 0.010 0.038 0
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0.407 -0.744 -0.099 0.403 1
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-0.010 0.367 0.021 -0.130 0
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0.675 -0.235 -0.073 0.486 1
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-0.385 -0.176 0.101 0.242 1
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-0.556 0.157 0.002 0.020 1
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0.217 -0.353 -0.056 0.366 1
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-0.042 -0.540 -0.033 0.420 0
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-0.014 -0.045 -0.008 -0.031 1
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0.357 -0.059 -0.025 0.312 1
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-0.154 0.207 0.023 -0.038 0
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-1.624 -0.570 0.006 0.049 1
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0.067 -0.154 -0.002 0.494 1
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-0.106 -0.427 -0.096 0.339 1
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-0.237 -0.313 -0.020 0.020 0
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-0.322 1.502 -0.048 -0.669 0
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-0.478 0.195 0.004 0.013 1
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-0.937 0.039 -0.039 -0.429 0
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-0.078 -0.205 -0.003 0.037 1
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-1.097 -0.204 0.045 -0.011 1
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0.080 0.201 0.120 -0.108 1
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0.004 -0.362 0.017 0.270 1
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0.878 0.305 -0.019 0.197 1
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1.108 -0.058 -0.033 0.175 1
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-0.637 -0.041 -0.090 -0.119 0
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-0.111 -0.322 -0.018 0.075 1
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-0.860 -0.325 0.045 -0.083 1
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-0.005 0.002 0.004 -0.010 1
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-0.947 -0.137 0.027 -0.207 0
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1.165 0.191 -0.010 -0.172 0
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-0.356 0.933 -0.008 -0.444 1
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-0.404 -0.507 0.035 0.276 1
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0.005 -0.376 -0.085 0.217 1
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-0.394 -0.341 -0.101 -0.207 0
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-0.293 0.766 0.063 -0.249 1
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-0.547 0.159 -0.013 -0.299 0
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-0.054 0.030 0.020 0.328 1
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-0.862 0.942 0.064 -0.360 1
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-0.147 -0.979 -0.026 0.514 1
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0.269 0.157 -0.004 0.037 0
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-0.042 -0.229 -0.007 0.081 0
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1.088 1.416 -0.001 -0.269 0
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-0.211 0.599 0.045 -0.316 0
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0.131 -0.550 -0.015 0.509 1
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-1.478 0.809 0.087 -0.895 0
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