def __init__(self, env, **kwargs):
def index(r, t):
return np.mean(r) + np.sqrt(np.log(t**2) / (2 * len(r)))
IndexAgent.__init__(self, env, index, **kwargs)
self.env = WriterWrapper(
self.env, self.writer, write_scalar="action_and_reward"
)
def __init__(self, env, **kwargs):
def index(r, t):
Na = len(r)
return np.mean(r) + np.sqrt(A / Na * max(0, np.log(T / (A * Na))))
IndexAgent.__init__(self, env, index, **kwargs)
self.env = WriterWrapper(
self.env, self.writer, write_scalar="action_and_reward"
)
def __init__(self, env, m=20, **kwargs):
def index(r, t):
if t < m * A:
index = -len(r) # select an action pulled the least
else:
index = np.mean(r, axis=0)
return index
IndexAgent.__init__(self, env, index, **kwargs)
self.env = WriterWrapper(
self.env, self.writer, write_scalar="action_and_reward"
)
def __init__(self, env, B=1, **kwargs):
def stat_function(stat, Na, action, reward):
# The statistic is the empirical mean. We compute it recursively.
if stat is None:
stat = np.zeros(len(Na))
stat[action] = (Na[action] - 1
) / Na[action] * stat[action] + reward / Na[action]
return stat
def index(stat, Na, t):
return stat + B * np.sqrt(2 * np.log(t**2) / Na)
RecursiveIndexAgent.__init__(self, env, stat_function, index, **kwargs)
self.env = WriterWrapper(self.env, self.writer, write_scalar="action")
def __init__(self, env, **kwargs):
def index(r, p, t):
return np.sum(1 - (1 - r) / p)
def prob(indices, t):
eta = np.minimum(np.sqrt(self.n_arms * np.log(self.n_arms) / (t + 1)), 1.0)
w = np.exp(eta * indices)
w /= w.sum()
return (1 - eta) * w + eta * np.ones(self.n_arms) / self.n_arms
RandomizedAgent.__init__(self, env, index, prob, **kwargs)
self.env = WriterWrapper(
self.env, self.writer, write_scalar="action_and_reward"
)
def __init__(self, env, **kwargs):
UCBVIAgent.__init__(self, env, **kwargs)
self.env = WriterWrapper(self.env, self.writer, write_scalar="invalid")
def __init__(self, env, **kwargs):
TSAgent.__init__(self, env, "beta", **kwargs)
self.env = WriterWrapper(self.env, self.writer, write_scalar="action")
def __init__(self, env, **kwargs):
UCBVIAgent.__init__(self, env, horizon=50, **kwargs)
self.env = WriterWrapper(self.env, self.writer, write_scalar="reward")
def __init__(self, env, **kwargs):
RecursiveIndexAgent.__init__(self, env,
**kwargs) # default is UCB for Bernoulli.
self.env = WriterWrapper(self.env, self.writer, write_scalar="action")