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@qorrect qorrect/main.py

Created Jun 26, 2020
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Trying to visualize the Environment in evaluate()
# Copyright 2018 Tensorforce Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import logging
import sys
import tensorflow as tf
from tensorforce.agents import Agent
from tensorforce.environments import Environment
from tensorforce.execution import Runner
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
def train(level, dir_name,num_episodes):
# Create an OpenAI-Gym environment
environment = Environment.create(
environment='gym', level=level, visualize=False)
# Create a PPO agent
agent = Agent.create(
agent='ppo',
environment=environment,
# Automatically configured network
network='auto',
# Optimization
batch_size=10,
update_frequency=2,
learning_rate=1e-3,
subsampling_fraction=0.2,
optimization_steps=5,
# Reward estimation
likelihood_ratio_clipping=0.2,
discount=0.99,
estimate_terminal=False,
# Critic
critic_network='auto',
critic_optimizer=dict(
optimizer='adam', multi_step=10, learning_rate=1e-3
),
# Preprocessing
preprocessing=None,
# Exploration
exploration=0.0, variable_noise=0.0,
# Regularization
l2_regularization=0.0, entropy_regularization=0.0,
# TensorFlow etc
name='agent',
device=None,
parallel_interactions=1,
seed=None,
execution=None,
saver=None,
summarizer=None,
recorder=None
)
# Initialize the runner
runner = Runner(agent=agent, environment=environment)
# Start the runner
runner.run(num_episodes=num_episodes)
runner.close()
modelDir = os.path.join(os.getcwd(), dir_name)
os.makedirs(modelDir)
agent.save(directory=modelDir)
def evaluate(level, dir_name,num_episodes):
environment = Environment.create(
environment='gym', level=level, visualize=True)
modelDir = os.path.join(os.getcwd(), dir_name)
agent = Agent.load(directory=modelDir, environment=environment)
runner = Runner(agent=agent, environment=level)
# Start the runner
runner.run(num_episodes=num_episodes)
runner.close()
if __name__ == '__main__':
level = 'CartPole-v0'
dir_name = f"models_{os.getpid()}"
num_episodes = 200
train(level, dir_name,num_episodes)
evaluate(level, dir_name,num_episodes)
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