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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Using SafeOpt
Step1:  Linear Car
Step2:  Below we output the results of the safety measure. List comprehension is used to get a more readable format for the
tuples.
The first element shows the standard deviation used, the second the number of violations and the last one the sum over
all violations, just as documented in the SafetyMeasure class.
We can see that increasing the standard deviation will ensure that the safty constraints will not be violated.
Step3:  Quadrocopter
Step4:  Below we output the results of the safety measure and performance. List comprehension is used to get a more readable format for the tuples.
The first element shows the standard deviation used, the second the number of violations and the last one the sum over
all violations, just as documented in the SafetyMeasure class.
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Python Code:
import GPy, safeopt
from SafeRLBench.algo import SafeOptSwarm
from SafeRLBench.envs import Quadrocopter, LinearCar
from SafeRLBench.policy import NonLinearQuadrocopterController, LinearPolicy
from SafeRLBench.measure import BestPerformance, SafetyMeasure
from SafeRLBench import Bench
# set up logging
from SafeRLBench import config
config.logger_set_level(config.INFO)
config.logger_add_stream_handler()
config.monitor_set_verbosity(2)
Explanation: Using SafeOpt
End of explanation
noise_var = 0.05 ** 2
bounds = [(-1., 0.), (-1., 0.), (0., 1.)]
algos = [(SafeOptSwarm, [{
    'policy': LinearPolicy(2, 1, par=[-1, 0, 1]),
    'kernel': GPy.kern.RBF(input_dim=len(bounds), variance=std**2, lengthscale=.4, ARD=True),
    'likelihood': GPy.likelihoods.gaussian.Gaussian(variance=noise_var),
    'max_it': 20,
    'avg_reward': -20,
    'window': 3,
    'fmin': -100,
    'bounds': bounds, 
    'info': std
} for std in [30, 35, 40, 45, 50]])]
envs = [(LinearCar, {})]
bench = Bench.make_bench(algos, envs, [BestPerformance(), SafetyMeasure(-100)])
bench()
Explanation: Linear Car
End of explanation
print([(t[0].alg_conf['info'], t[1], t[2]) for t in bench.measures[1].result])
Explanation: Below we output the results of the safety measure. List comprehension is used to get a more readable format for the
tuples.
The first element shows the standard deviation used, the second the number of violations and the last one the sum over
all violations, just as documented in the SafetyMeasure class.
We can see that increasing the standard deviation will ensure that the safty constraints will not be violated.
End of explanation
noise_var = 0.05 ** 2
# Set fixed Gaussian measurement noise
likelihood = GPy.likelihoods.gaussian.Gaussian(variance=noise_var)
# Bounds on the inputs variable
bounds = [(0., 1.), (0., 1.), (0., 1.), (0., 1.), (0., 1.)]
# Define Kernel
kernel = GPy.kern.RBF(input_dim=len(bounds), variance=1000.*2, lengthscale=1.0, ARD=True)
noise_var = 0.05 ** 2
fmin = -2400
# Bounds on the inputs variable
# bounds = [(1e-2, .9), (1e-2, .9), (1e-1, .9), (.2, .7), (1e-2, .9)]
bounds = [(1e-2, 1.), (1e-2, 1.), (1e-2, 1.), (1e-2, 1.), (1e-2, 1.)]
algos = [(SafeOptSwarm, [{
    'policy': NonLinearQuadrocopterController(),
    'kernel': GPy.kern.RBF(input_dim=len(bounds), variance=std**2, lengthscale=0.2, ARD=True),
    'likelihood': GPy.likelihoods.gaussian.Gaussian(variance=noise_var),
    'max_it': 20,
    'avg_reward': -1500,
    'window': 3,
    'fmin': fmin,
    'bounds': bounds,
    'swarm_size': 1000,
    'info': std
} for std in [1000, 1250, 1500, 1750, 2000]])]
envs = [(Quadrocopter, {})]
bench = Bench.make_bench(algos, envs, [BestPerformance(), SafetyMeasure(fmin)])
bench()
Explanation: Quadrocopter
End of explanation
print([(t[0].alg_conf['info'], t[1], t[2]) for t in bench.measures[1].result])
print([(t[0].alg_conf['info'], int(t[1])) for t in bench.measures[0].result])
Explanation: Below we output the results of the safety measure and performance. List comprehension is used to get a more readable format for the tuples.
The first element shows the standard deviation used, the second the number of violations and the last one the sum over
all violations, just as documented in the SafetyMeasure class.
End of explanation
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