# 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.
# ==============================================================================
from tensorforce.environments import Environment
[docs]class OpenSim(Environment):
"""
[OpenSim](http://osim-rl.stanford.edu/) environment adapter (specification key: `osim`,
`open_sim`).
Args:
level ('Arm2D' | 'L2Run' | 'Prosthetics'): Environment id
(<span style="color:#C00000"><b>required</b></span>).
visualize (bool): Whether to visualize interaction
(<span style="color:#00C000"><b>default</b></span>: false).
integrator_accuracy (float): Integrator accuracy
(<span style="color:#00C000"><b>default</b></span>: 5e-5).
"""
@classmethod
def levels(cls):
return ['Arm2D', 'L2Run', 'Prosthetics']
def __init__(self, level, visualize=False, integrator_accuracy=5e-5):
super().__init__()
from osim.env import L2RunEnv, Arm2DEnv, ProstheticsEnv
environments = dict(Arm2D=Arm2DEnv, L2Run=L2RunEnv, Prosthetics=ProstheticsEnv)
self.environment = environments[level](
visualize=visualize, integrator_accuracy=integrator_accuracy
)
def __str__(self):
return super().__str__() + '({})'.format(self.environment)
def states(self):
return dict(type='float', shape=self.environment.get_observation_space_size())
def actions(self):
return dict(type='float', shape=self.environment.get_action_space_size())
def close(self):
self.environment.close()
self.environment = None
def reset(self):
return self.environment.reset()
def execute(self, actions):
states, reward, terminal, _ = self.env.step(action=actions)
return states, terminal, reward