def main():
try:
shutil.rmtree('images')
print("delete images directory")
except OSError as e:
print("Error: %s : %s" % ('images', e.strerror))
gym.logger.set_level(INFO)
start_date = date(2019, 5, 1)
simulate_company_list = [2, 3, 4, 5, 6, 44, 300, 67, 100, 200]
# simulate_company_list = [3]
env = gym.make("AsxGym-v0",
start_date=start_date,
simulate_company_list=simulate_company_list)
stock_agent = RandomAgent(env)
# stock_agent = RandomAgent(env, min_volume=100, max_volume=500)
# stock_agent = BuyAndKeepAgent(env, 3)
observation = env.reset()
for _ in range(200000 * 24):
env.render()
company_count = len(env.simulate_company_list)
observation, reward, done, info = env.step(stock_agent.action())
if done:
env.insert_summary_images(30)
observation = env.reset()
stock_agent.reset()
if observation is not None:
asx_observation = AsxObservation(observation)
print(asx_observation.to_json_obj())
print(info)
env.close()
def run(env_name, agent_name, nb_episodes, render_freq, render_mode):
logger.set_level(logger.INFO)
env = gym.make(env_name)
# You provide the directory to write to (can be an existing
# directory, including one with existing data -- all monitor files
# will be namespaced). You can also dump to a tempdir if you'd
# like: tempfile.mkdtemp().
#outdir = '/tmp/random-agent-results'
#video_callable = None if render_mode == 'human' else False
#env = wrappers.Monitor(env, directory=outdir, force=True, video_callable=video_callable)
#env = DynamicMonitor(env, directory=outdir, force=True, video_callable=video_callable)
env.render(mode=render_mode)
env.seed(0)
if agent_name == 'RandomAgent':
agent = RandomAgent(env.env.action_space)
elif agent_name == 'EpsilonGreedyAgent':
agent = EpsilonGreedy(env.env.action_space)
elif agent_name == 'GradientBanditAgent':
agent = GradientBandit(env.env.action_space)
elif agent_name == 'ucb':
agent = ucb(env.env.action_space)
elif agent_name == 'ThompsonSampling':
agent = ThompsonSampling(env.env.action_space)
step = 0
reward = 0
done = False
for episode in range(nb_episodes):
print(f'--------- Episode {episode} ---------')
ob = env.reset()
agent = agent.reset()
while True:
step += 1
# action space may have change
# agent = EpsilonGreedy(env.env.action_space)
action = agent.act(ob, reward, done)
ob, reward, done, _ = env.step(action)
if done:
break
if step % render_freq == 0:
env.render()
# Note there's no env.render() here. But the environment still can open window and
# render if asked by env.monitor: it calls env.render('rgb_array') to record video.
# Video is not recorded every episode, see capped_cubic_video_schedule for details.
# Close the env and write monitor result info to disk
env.env.close()
# from agents.buy_and_keep_agent import BuyAndKeepAgenta
from agents.random_agent import RandomAgent
from asx_gym.envs import AsxObservation
gym.logger.set_level(INFO)
start_date = date(2019, 5, 1)
simulate_company_list = [2, 3, 4, 5, 6, 44, 300, 67, 100, 200]
# simulate_company_list = [3]
env = gym.make("AsxGym-v0",
start_date=start_date,
simulate_company_list=simulate_company_list)
stock_agent = RandomAgent(env)
# stock_agent = RandomAgent(env, min_volume=100, max_volume=500)
# stock_agent = BuyAndKeepAgent(env, 3)
observation = env.reset()
for _ in range(200000 * 24):
env.render()
company_count = len(env.simulate_company_list)
observation, reward, done, info = env.step(stock_agent.action())
if done:
env.insert_summary_images(30)
observation = env.reset()
stock_agent.reset()
if observation is not None:
asx_observation = AsxObservation(observation)
print(asx_observation.to_json_obj())
print(info)
env.close()
