def test_run(self):
# Set up environment and policy
env = gym.make('MountainCar-v0')
pol = vcf.DiscreteRandomControl(env.action_space.n)
# Define an agent
phi_1 = vcf.BiasUnit()
td_1 = vcf.TD(len(phi_1))
params_1 = {
'alpha': 0.01,
'gm': 0.999,
'gm_p': 0.999,
'lm': 0.1,
}
agent_1 = vcf.Agent(td_1, phi_1, params_1)
agents = [agent_1]
# Set up the experiment
experiment = vcf.LiveExperiment(env, pol, agents)
# Try running the experiment
num_eps = 10
max_steps = 10
experiment.run(num_eps, max_steps, callbacks=[])
def test_run_with_callbacks(self):
# Set up environment and policy
env = gym.make('MountainCar-v0')
pol = vcf.DiscreteRandomControl(env.action_space.n)
# Define an agent
phi_1 = vcf.BiasUnit()
td_1 = vcf.TD(len(phi_1))
params_1 = {
'alpha': 0.01,
'gm': 0.999,
'gm_p': 0.999,
'lm': 0.1,
}
agent_1 = vcf.Agent(td_1, phi_1, params_1)
agents = [agent_1]
# Set up the experiment
experiment = vcf.LiveExperiment(env, pol, agents)
# Set up testing callbacks
cbk = _CheckCallback()
# Try running the experiment
num_eps = 10
max_steps = 10
experiment.run(num_eps, max_steps, callbacks=[cbk])
# Check that the callbacks ran properly
assert (cbk.experiment_begin > 0)
assert (cbk.experiment_end > 0)
assert (cbk.episode_begin > 0)
assert (cbk.episode_end > 0)
assert (cbk.step_begin > 0)
assert (cbk.step_end > 0)
tiling_2 = vcf.features.BinaryTiling(env.observation_space, 19)
tiling_3 = vcf.features.BinaryTiling(env.observation_space, 31)
# Concatenate binary vectors
phi = vcf.Union(tiling_1, tiling_2, tiling_3)
# Define the control (discrete actions Q-learning)
dq = vcf.DiscreteQ(len(phi), na, epsilon=0.002)
dq_params = {
'alpha' : vcf.parameters.EpisodicPowerLaw(0.2, 0.25),
'gm' : 0.9999,
'gm_p' : vcf.Constant(0.9999, 0),
'lm' : vcf.Constant(0.5, 0),
}
control = vcf.Agent(dq, phi, dq_params)
# List of agents to update
learners = [control]
# Set up the experiment
experiment = vcf.LiveExperiment(env, control, learners=learners)
# Set up callbacks
hist_cbk = vcf.callbacks.History()
cbk_lst = [
vcf.callbacks.Progress(),
hist_cbk,
]
# Run the experiment
experiment.run(150, 2000, callbacks=cbk_lst)
def test_init(self):
env = gym.make('MountainCar-v0')
pol = vcf.DiscreteRandomControl(env.action_space.n)
agents = []
experiment = vcf.LiveExperiment(env, pol, agents)