Source code for ray.rllib.utils.filter

import logging
import numpy as np
import threading

logger = logging.getLogger(__name__)


[docs]class Filter: """Processes input, possibly statefully."""
[docs] def apply_changes(self, other, *args, **kwargs): """Updates self with "new state" from other filter.""" raise NotImplementedError
[docs] def copy(self): """Creates a new object with same state as self. Returns: A copy of self. """ raise NotImplementedError
[docs] def sync(self, other): """Copies all state from other filter to self.""" raise NotImplementedError
[docs] def clear_buffer(self): """Creates copy of current state and clears accumulated state""" raise NotImplementedError
def as_serializable(self): raise NotImplementedError
class NoFilter(Filter): is_concurrent = True def __init__(self, *args): pass def __call__(self, x, update=True): try: return np.asarray(x) except Exception: raise ValueError("Failed to convert to array", x) def apply_changes(self, other, *args, **kwargs): pass def copy(self): return self def sync(self, other): pass def clear_buffer(self): pass def as_serializable(self): return self # http://www.johndcook.com/blog/standard_deviation/ class RunningStat: def __init__(self, shape=None): self._n = 0 self._M = np.zeros(shape) self._S = np.zeros(shape) def copy(self): other = RunningStat() other._n = self._n other._M = np.copy(self._M) other._S = np.copy(self._S) return other def push(self, x): x = np.asarray(x) # Unvectorized update of the running statistics. if x.shape != self._M.shape: raise ValueError( "Unexpected input shape {}, expected {}, value = {}".format( x.shape, self._M.shape, x)) n1 = self._n self._n += 1 if self._n == 1: self._M[...] = x else: delta = x - self._M self._M[...] += delta / self._n self._S[...] += delta * delta * n1 / self._n def update(self, other): n1 = self._n n2 = other._n n = n1 + n2 if n == 0: # Avoid divide by zero, which creates nans return delta = self._M - other._M delta2 = delta * delta M = (n1 * self._M + n2 * other._M) / n S = self._S + other._S + delta2 * n1 * n2 / n self._n = n self._M = M self._S = S def __repr__(self): return "(n={}, mean_mean={}, mean_std={})".format( self.n, np.mean(self.mean), np.mean(self.std)) @property def n(self): return self._n @property def mean(self): return self._M @property def var(self): return self._S / (self._n - 1) if self._n > 1 else np.square(self._M) @property def std(self): return np.sqrt(self.var) @property def shape(self): return self._M.shape class MeanStdFilter(Filter): """Keeps track of a running mean for seen states""" is_concurrent = False def __init__(self, shape, demean=True, destd=True, clip=10.0): self.shape = shape self.demean = demean self.destd = destd self.clip = clip self.rs = RunningStat(shape) # In distributed rollouts, each worker sees different states. # The buffer is used to keep track of deltas amongst all the # observation filters. self.buffer = RunningStat(shape) def clear_buffer(self): self.buffer = RunningStat(self.shape) def apply_changes(self, other, with_buffer=False): """Applies updates from the buffer of another filter. Params: other (MeanStdFilter): Other filter to apply info from with_buffer (bool): Flag for specifying if the buffer should be copied from other. Examples: >>> a = MeanStdFilter(()) >>> a(1) >>> a(2) >>> print([a.rs.n, a.rs.mean, a.buffer.n]) [2, 1.5, 2] >>> b = MeanStdFilter(()) >>> b(10) >>> a.apply_changes(b, with_buffer=False) >>> print([a.rs.n, a.rs.mean, a.buffer.n]) [3, 4.333333333333333, 2] >>> a.apply_changes(b, with_buffer=True) >>> print([a.rs.n, a.rs.mean, a.buffer.n]) [4, 5.75, 1] """ self.rs.update(other.buffer) if with_buffer: self.buffer = other.buffer.copy() def copy(self): """Returns a copy of Filter.""" other = MeanStdFilter(self.shape) other.sync(self) return other def as_serializable(self): return self.copy() def sync(self, other): """Syncs all fields together from other filter. Examples: >>> a = MeanStdFilter(()) >>> a(1) >>> a(2) >>> print([a.rs.n, a.rs.mean, a.buffer.n]) [2, array(1.5), 2] >>> b = MeanStdFilter(()) >>> b(10) >>> print([b.rs.n, b.rs.mean, b.buffer.n]) [1, array(10.0), 1] >>> a.sync(b) >>> print([a.rs.n, a.rs.mean, a.buffer.n]) [1, array(10.0), 1] """ assert other.shape == self.shape, "Shapes don't match!" self.demean = other.demean self.destd = other.destd self.clip = other.clip self.rs = other.rs.copy() self.buffer = other.buffer.copy() def __call__(self, x, update=True): x = np.asarray(x) if update: if len(x.shape) == len(self.rs.shape) + 1: # The vectorized case. for i in range(x.shape[0]): self.rs.push(x[i]) self.buffer.push(x[i]) else: # The unvectorized case. self.rs.push(x) self.buffer.push(x) if self.demean: x = x - self.rs.mean if self.destd: x = x / (self.rs.std + 1e-8) if self.clip: x = np.clip(x, -self.clip, self.clip) return x def __repr__(self): return "MeanStdFilter({}, {}, {}, {}, {}, {})".format( self.shape, self.demean, self.destd, self.clip, self.rs, self.buffer) class ConcurrentMeanStdFilter(MeanStdFilter): is_concurrent = True def __init__(self, *args, **kwargs): super(ConcurrentMeanStdFilter, self).__init__(*args, **kwargs) self._lock = threading.RLock() def lock_wrap(func): def wrapper(*args, **kwargs): with self._lock: return func(*args, **kwargs) return wrapper self.__getattribute__ = lock_wrap(self.__getattribute__) def as_serializable(self): """Returns non-concurrent version of current class""" other = MeanStdFilter(self.shape) other.sync(self) return other def copy(self): """Returns a copy of Filter.""" other = ConcurrentMeanStdFilter(self.shape) other.sync(self) return other def __repr__(self): return "ConcurrentMeanStdFilter({}, {}, {}, {}, {}, {})".format( self.shape, self.demean, self.destd, self.clip, self.rs, self.buffer) def get_filter(filter_config, shape): # TODO(rliaw): move this into filter manager if filter_config == "MeanStdFilter": return MeanStdFilter(shape, clip=None) elif filter_config == "ConcurrentMeanStdFilter": return ConcurrentMeanStdFilter(shape, clip=None) elif filter_config == "NoFilter": return NoFilter() else: raise Exception("Unknown observation_filter: " + str(filter_config))