def play_live():
live = TV.get_live()
if live is not None:
shared_data = SharedData()
shared_data.set('playing', {
'what': 'nba_tv_live',
})
common.play(live)
def play_serieepisode():
episode = TV.get_serie_episode()
if episode is not None:
shared_data = SharedData()
shared_data.set('playing', {
'what': 'episode_nba_tv',
})
common.play(episode)
def play_episode():
start_timestamp = vars.params.get('start_timestamp')
duration = vars.params.get('duration')
episode = TV.get_episode(start_timestamp, duration)
if episode is not None:
shared_data = SharedData()
shared_data.set('playing', {
'what': 'nba_tv_episode',
'data': {
'start_timestamp': start_timestamp,
'duration': duration,
},
})
common.play(episode)
def play_game():
if not common.authenticate():
return
currentvideo_id = vars.params.get("video_id")
currentvideo_type = vars.params.get("video_type")
start_time = vars.params.get("start_time")
duration = vars.params.get("duration")
currentvideo_ishomefeed = vars.params.get("video_ishomefeed", "1")
currentvideo_ishomefeed = currentvideo_ishomefeed == "1"
# Authentication is needed over this point!
game = get_game(currentvideo_id, currentvideo_type, currentvideo_ishomefeed, start_time, duration)
if game is not None:
common.play(game)
def play_one_game():
client = MongoClient()
db = client["sandbox"]
solutions = list(db.solutions.find())
if len(solutions) < 2:
print("Not enough solutions to play")
return
solutions = random.sample(solutions, 2)
# print solutions
binaries = [s["binary"] for s in solutions]
print("Playing {}".format(binaries))
winner = play(*binaries)
print("Winner: {}".format(winner))
if winner == 0:
# draw
solution = solutions[0]["solution"]
db.results.update_one({"solution": solution},
{"$inc": {
"wins.{}".format(0): 1
}})
solution = solutions[1]["solution"]
db.results.update_one({"solution": solution},
{"$inc": {
"wins.{}".format(0): 1
}})
else:
assert (winner in (1, 2))
solution = solutions[0]["solution"]
db.results.update_one({"solution": solution},
{"$inc": {
"wins.{}".format(winner): 1
}})
solution = solutions[1]["solution"]
db.results.update_one({"solution": solution},
{"$inc": {
"wins.{}".format(3 - winner): 1
}})
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# dynamic-graph is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Lesser Public License for more details. You should have
# received a copy of the GNU Lesser General Public License along with
# dynamic-graph. If not, see <http://www.gnu.org/licenses/>.
from dynamic_graph.sot.dynamics.tools import *
from dynamic_graph.sot.motion_planner. \
test.feet_follower_from_file_walk_on_a_thread import *
from common import play
play(f)
finalPosition = (
-0.015361, -0.0049075500000000001, -0.00047065200000000001, -0.0172946,
-0.020661800000000001, 0.0374547, -0.037641599999999997,
0.025434399999999999, -0.45398100000000002, 0.86741800000000002,
-0.39213799999999999, -0.0089269499999999995, -0.037646100000000002,
0.025648199999999999, -0.46715499999999999, 0.87717599999999996,
-0.38872200000000001, -0.0091408199999999992, 0.080488199999999996,
-0.18355399999999999, -0.00036695100000000002, -0.0056776600000000002,
-0.12173299999999999, -0.23972599999999999, -0.00637303,
-0.56908000000000003, 0.00296262, 0.19108900000000001, 0.100088,
0.23896800000000001, 0.21485599999999999, -0.18973400000000001,
-0.49457699999999999, 0.040646799999999997, 0.16970299999999999, 0.100067)
checkFinalConfiguration(robot.device.state.value, finalPosition)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2011, Florent Lamiraux, Thomas Moulard, JRL, CNRS/AIST
#
# This file is part of sot-motion-planner.
# sot-motion-planner is free software: you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public License
# as published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# sot-motion-planner is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Lesser Public License for more details. You should have
# received a copy of the GNU Lesser General Public License along with
# sot-motion-planner. If not, see <http://www.gnu.org/licenses/>.
from dynamic_graph.sot.dynamics.tools import *
from dynamic_graph.sot.motion_planner. \
test.feet_follower_analytical_pg_correction import *
from common import play; play(f, maxIter=1000*12)
f.trace.dump()
@author: mingrui
"""
import sys
import torch
from common import get_env, play
from dqn_agent import Agent
if __name__ == '__main__':
if len(sys.argv) == 1:
dqn_fname = None
else:
dqn_fname = sys.argv[1]
# get env
env, state_size, action_size = get_env()
# load and play with trained agent
agent = None
if dqn_fname is not None:
agent = Agent(state_size, action_size, seed=0)
agent.dqn_local.load_state_dict(torch.load(dqn_fname))
print('Playing with agent {}...'.format(dqn_fname))
play(env, agent)
else:
# play ranomly
print('Playing randomly...')
play(env)
env.close()