Getting Started

This is an Notebook containing the examples from the Getting Started section in the documentation. Refer to the documentation for very verbose description of this code.

Optimizing a Policy



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# import the classes we need
from SafeRLBench.envs import LinearCar
from SafeRLBench.policy import LinearPolicy
from SafeRLBench.algo import PolicyGradient



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# get an instance of `LinearCar` with the default arguments.
linear_car = LinearCar()
# we need a policy which maps R^2 to R
policy = LinearPolicy(2, 1)
# setup parameters
policy.parameters = [-1, -1, 1]

# plug the environment and policy into the algorithm
optimizer = PolicyGradient(linear_car, policy, estimator='central_fd')

# run optimization
optimizer.optimize()

Lets take a look at what happened during the run. For this we can access the monitor and generate some plots.



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import matplotlib.pyplot as plt

y = optimizer.monitor.rewards

plt.plot(range(len(y)), y)
plt.show()

Configuration



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# import the configuration object
from SafeRLBench import config



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# setup stream handler
config.logger_add_stream_handler()
# setup logger level
config.logger_set_level(config.DEBUG)
# raise monitor verbosity
config.monitor_set_verbosity(2)

After changing these values, please run the cell which invokes optimizer.optimize again to see what happens.

Benchmark



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# import the best performance measure
from SafeRLBench.measure import BestPerformance
# import the Bench and BenchConfig
from SafeRLBench import Bench, BenchConfig



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# define environment configuration.
envs = [[(LinearCar, {'horizon': 100})]]
# define algorithms configuration.
algs = [[
  (PolicyGradient, [{
    'policy': LinearPolicy(2, 1, par=[-1, -1, 1]),
    'estimator': 'central_fd',
    'var': var
  } for var in [1, 1.5, 2, 2.5]])
]]

# instantiate BenchConfig
config = BenchConfig(algs, envs)

# instantiate the bench
bench = Bench(config, BestPerformance())

# configure to run in parallel
config.jobs_set(4)



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bench()



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bench.measures[0]



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best_run = bench.measures[0].result[0][0]
monitor = best_run.get_alg_monitor()
best_trace = monitor.traces[monitor.rewards.index(max(monitor.rewards))]
y = [t[1][0] for t in best_trace]
x = range(len(y))

import matplotlib.pyplot as plt

plt.plot(x, y)
plt.show()



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