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random_agent.py
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random_agent.py
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# Copyright 2020 The fingym Authors.
#
# 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.
from __future__ import division
import argparse
from fingym import fingym
class RandomAgent(object):
def __init__(self, action_space):
self.action_space = action_space
def act(self, observation, reward, done):
return self.action_space.sample()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=None)
parser.add_argument('env_id', nargs='?', default='SPY-Daily-v0', help='Select the environment to run')
args = parser.parse_args()
env = fingym.make(args.env_id)
agent = RandomAgent(env.action_space)
episode_count = 100
reward = 0
done = False
final_vals = []
initial_value = 0
for i in range(episode_count):
ob = env.reset()
initial_value = ob[1]
while True:
action = agent.act(ob, reward, done)
ob, reward, done, info = env.step(action)
if done:
final_vals.append(info['cur_val'])
break
max_value = max(final_vals)
min_value = min(final_vals)
avg_value = sum(final_vals)/len(final_vals)
print('initial value: {}'.format(initial_value))
print('min_value: {}, avg_value: {}, max_value: {}'.format(min_value,avg_value,max_value))