added BO torch tryout

.idea/ActiveBOToytask.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.8 (venv)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (RlToyTask)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
 </project>

BoTorchTest/botorchtest1.py

@@ -0,0 +1,26 @@
+import torch
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll
+from botorch.utils import standardize
+from gpytorch.mlls import ExactMarginalLogLikelihood
+
+from botorch.acquisition import UpperConfidenceBound
+from botorch.optim import optimize_acqf
+
+
+train_X = torch.rand(10, 2, dtype=torch.double)
+Y = 1 - torch.norm(train_X - 0.5, dim=-1, keepdim=True)
+Y = Y + 0.1 * torch.randn_like(Y)  # add some noise
+train_Y = standardize(Y)
+
+gp = SingleTaskGP(train_X, train_Y)
+mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+fit_gpytorch_mll(mll)
+
+UCB = UpperConfidenceBound(gp, beta=0.1)
+
+bounds = torch.stack([torch.zeros(2), torch.ones(2)])
+candidate, acq_value = optimize_acqf(
+    UCB, bounds=bounds, q=1, num_restarts=5, raw_samples=20,
+)
+print(candidate)

runner/BOGymRunner.py

@@ -5,10 +5,10 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 # BO parameters
-env  = Continuous_MountainCarEnv()
+env = Continuous_MountainCarEnv()
 nr_steps = 100
 acquisition_fun = 'ei'
-iteration_steps = 200
+iteration_steps = 100
 
 nr_runs = 100