tomascsantos

anyio==3.5.0
appdirs==1.4.4
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
attrs==21.4.0
Babel==2.9.1
backcall==0.2.0
backports.zoneinfo==0.2.1
beautifulsoup4==4.10.0
bleach==4.1.0

tomascsantos

anyio==3.5.0
appnope==0.1.3
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
asgiref==3.5.2
attrs==21.4.0
autobahn==22.4.2
Automat==20.2.0
Babel==2.10.1
backcall==0.2.0

tomascsantos

alert-windows==0.6.1
appdirs==1.4.4
backcall==0.2.0
bleach==4.1.0
bokeh==2.4.2
bokehtools==0.20.1
boto3==1.18.13
botocore==1.21.64
bouncer-client==0.4.0
caspian==2.1.10.post8829.dev0

tomascsantos

anyio==3.5.0
appnope==0.1.3
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
asgiref==3.5.2
attrs==21.4.0
autobahn==22.4.2
Automat==20.2.0
Babel==2.10.1
backcall==0.2.0

tomascsantos

anyio==3.5.0
appdirs==1.4.4
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
attrs==21.4.0
Babel==2.9.1
backcall==0.2.0
backports.zoneinfo==0.2.1
beautifulsoup4==4.10.0
bleach==4.1.0

tomascsantos

osx-64:
      - anyio=3.5.0=py39h6e9494a_0
      - appnope=0.1.3=pyhd8ed1ab_0
      - argon2-cffi-bindings=21.2.0=py39h89e85a6_1
      - bokeh=2.4.2=py39h6e9494a_0
      - brotli-bin=1.0.9=h5eb16cf_7
      - brotli=1.0.9=h5eb16cf_7
      - brotlipy=0.7.0=py39h63b48b0_1004
      - bzip2=1.0.8=h0d85af4_4
      - c-ares=1.18.1=h0d85af4_0

tomascsantos

class FFModel(nn.Module, BaseModel):

    def __init__(self, ac_dim, ob_dim, n_layers, size, learning_rate=0.001):
        super(FFModel, self).__init__()

        self.ac_dim = ac_dim
        self.ob_dim = ob_dim
        self.n_layers = n_layers
        self.size = size
        self.learning_rate = learning_rate

tomascsantos

old net:  Sequential(
  (0): Linear(in_features=111, out_features=64, bias=True)
  (1): Tanh()
  (2): Linear(in_features=64, out_features=64, bias=True)
  (3): Tanh()
  (4): Linear(in_features=64, out_features=8, bias=True)
  (5): Identity()
)
Loading expert policy from... ../policies/experts/Ant.pkl
obs (1, 111) (1, 111)

tomascsantos

C = np.array([
  [0,1],
  [0,-1],
  [1,0],
  [-1,0]
])
lam2 = opti.variable(C.shape[0], self.H+1) #dual variables for obstacle opt
Rot = lambda theta : np.array([
  [casadi.cos(theta), -casadi.sin(theta)],
  [casadi.sin(theta), casadi.cos(theta)],

tomascsantos

def displayImage(imgArray, original):
    #import original
    original = plt.imread(f).astype(np.uint8)
    #reshape modified to match original
    imgArray = np.reshape(imgArray, original.shape)
    #make sure the type is correct
    imgArray = imgArray.astype(np.uint8)
    newImg = Image.fromarray(imgArray)
    #prepare figure: