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actors.py
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"""
Deterministic Actor Functions:
"""
from layers import *
TINY = 1e-7
# -------------------------------------------------------------------------#
# Noise
def ou_noise(trng, x, mu=0., theta=0.15, sigma=0.01):
dx = theta * (mu - x) + sigma * trng.normal(x.shape)
return x + dx
def gaussian_noise(trng, x, mu=0, sigma=0.01):
dx = mu + sigma * trng.normal(x.shape)
return dx
# -------------------------------------------------------------------------#
# Actors:
actors = dict()
actors['dumb'] = ('param_init_dumb', 'dumb_actor')
actors['const'] = ('param_init_constant', 'constant_actor')
actors['ff'] = ('param_init_ff', 'ff_actor')
actors['gru'] = ('param_init_gru', 'gru_actor', 'gru_actor_hard')
actors['gru2'] = ('param_init_gru2', 'gru_actor2')
actors['gg'] = ('param_init_gg', 'gg_actor')
def get_actor(name):
fns = actors[name]
return tuple([eval(f) for f in fns])
def _p(pp, name):
return '%s_%s' % (pp, name)
# -------------------------------------------------------------------------#
# Dump Actors:
def param_init_dumb(options, prefix='db', nin=None, nout=None):
params = OrderedDict()
if nin is None:
nin = options['dim'] + options['ctxdim']
if nout is None:
nout = options['dim']
return params
def dumb_actor(tparams, options,h1, ctx=None, act=None, prefix='db'):
action = tensor.zeros_like(h1)
hidden = act
return action, hidden
# constant Actors:
def param_init_constant(options, prefix='ct', nin=None, nout=None):
params = OrderedDict()
if nin is None:
nin = options['dim'] + options['ctxdim']
if nout is None:
nout = options['dim']
params[_p(prefix, 'a')] = numpy.zeros((nout,)).astype('float32')
return params
def constant_actor(tparams, options, h1, ctx=None, act=None, prefix='ct'):
action = tensor.zeros_like(h1)
if action.ndim == 2:
action += tparams[_p(prefix, 'a')][None, :]
elif action.ndim == 3:
action += tparams[_p(prefix, 'a')][None, None, :]
else:
action += tparams[_p(prefix, 'a')]
hidden = act
return action, hidden
# Feedforward Actors:
def param_init_ff(options, prefix='ff', nin=None, nout=None, nhid=None):
params = OrderedDict()
if nin is None:
nin = options['dim'] + options['ctxdim']
if nout is None:
nout = options['dim']
if nhid is None:
nhid = options['act_hdim']
params = get_layer('ff')[0](options, params, prefix=prefix + '_in',
nin=nin, nout=nhid, scale=0.001)
params = get_layer('ff')[0](options, params, prefix=prefix + '_out',
nin=nhid, nout=nout, scale=0.001)
return params
def ff_actor(tparams, options, h1, ctx=None, act=None, prefix='ff'):
hidden = get_layer('ff')[1](tparams, concatenate([h1, ctx], axis=1),
options, prefix=prefix + '_in', activ='tanh')
action = get_layer('ff')[1](tparams, hidden,
options, prefix=prefix + '_out', activ='tanh')
return action, hidden
# Recurrent Actors:
def param_init_gru(options, prefix='ff', nin=None, nout=None, nhid=None):
params = OrderedDict()
if nin is None:
nin = 2 * options['dim'] + options['ctxdim']
if nout is None:
nout = options['dim']
if nhid is None:
nhid = options['act_hdim']
# params = get_layer('lngru')[0](options, params, prefix=prefix + '_in',
# nin=nin, dim=nhid, scale=0.001)
params = get_layer('gru')[0](options, params, prefix=prefix + '_in',
nin=nin, dim=nhid, scale=0.001)
params = get_layer('ff')[0](options, params, prefix=prefix + '_out',
nin=nhid, nout=nout, scale=0.001)
return params
def gru_actor(tparams, options, h1, ctx=None, act=None, prefix='ff'):
pre_state, pre_action = act[:, :options['act_hdim']], act[:, options['act_hdim']:]
# hidden = get_layer('lngru')[1](tparams, concatenate([h1, ctx, pre_action], axis=1),
# options, prefix=prefix + '_in',
# one_step=True, _init_state=pre_state)[0]
hidden = get_layer('gru')[1](tparams, concatenate([h1, ctx, pre_action], axis=1),
options, prefix=prefix + '_in',
one_step=True, _init_state=pre_state)[0]
action = get_layer('ff')[1](tparams, hidden,
options, prefix=prefix + '_out', activ='tanh')
cur_act = concatenate([hidden, action], axis=1)
return action, cur_act
# Recurrent Actor2
def param_init_gru2(options, prefix='ff', nin=None, nout=None, nhid=None):
params = OrderedDict()
if nin is None:
nin = options['dim']
if nout is None:
nout = options['dim']
if nhid is None:
nhid = options['act_hdim']
# params = get_layer('lngru')[0](options, params, prefix=prefix + '_in',
# nin=nin, dim=nhid, scale=0.001)
params = get_layer('gru')[0](options, params, prefix=prefix + '_in',
nin=nin, dim=nhid, scale=0.001)
params = get_layer('ff')[0](options, params, prefix=prefix + '_out',
nin=nhid, nout=nout, scale=0.001)
return params
def gru_actor2(tparams, options, h1, act=None, prefix='ff'):
# hidden = get_layer('lngru')[1](tparams, concatenate([h1, ctx, pre_action], axis=1),
# options, prefix=prefix + '_in',
# one_step=True, _init_state=pre_state)[0]
hidden = get_layer('gru')[1](tparams, h1,
options, prefix=prefix + '_in',
one_step=True, _init_state=act)[0]
action = get_layer('ff')[1](tparams, hidden,
options, prefix=prefix + '_out', activ='tanh')
return action, hidden
def gru_actor_hard(tparams, options, h1, ctx=None, act=None, prefix='ff', bound=0.1):
pre_state, pre_action = act[:, :options['act_hdim']], act[:, options['act_hdim']:]
# hidden = get_layer('lngru')[2](tparams, concatenate([h1, ctx, pre_action], axis=1),
# options, prefix=prefix + '_in',
# one_step=True, _init_state=pre_state)[0]
hidden = get_layer('gru')[1](tparams, concatenate([h1, ctx, pre_action], axis=1),
options, prefix=prefix + '_in',
one_step=True, _init_state=pre_state)[0]
action = get_layer('ff')[1](tparams, hidden,
options, prefix=prefix + '_out', activ='tanh')
a_norm = tensor.sqrt(tensor.sum(action ** 2, axis=-1, keepdims=True))
action = tensor.switch(a_norm > bound, action / a_norm * bound, action) # add a hard boundary of actions
cur_act = concatenate([hidden, action], axis=1)
return action, cur_act
# Recurrent Actors:
def param_init_gg(options, prefix='ff', nin=None, nout=None, nhid=None):
params = OrderedDict()
if nin is None:
nin = 2 * options['dim'] + options['ctxdim']
if nout is None:
nout = options['dim']
if nhid is None:
nhid = options['act_hdim']
# params = get_layer('lngru')[0](options, params, prefix=prefix + '_in',
# nin=nin, dim=nhid, scale=0.001)
params = get_layer('gru')[0](options, params, prefix=prefix + '_in',
nin=nin, dim=nhid, scale=0.001)
params = get_layer('ff')[0](options, params, prefix=prefix + '_out',
nin=nhid, nout=nout, scale=0.001)
# params = get_layer('ff')[0](options, params, prefix=prefix + '_gate',
# nin=nhid + nout, nout=1)
# params = get_layer('ff')[0](options, params, prefix=prefix + '_gate',
# nin=nin + nout, nout=1)
params = get_layer('ff')[0](options, params, prefix=prefix + '_gate',
nin=nin + nout, nout=nout)
return params
def gg_actor(tparams, options, h1, ctx=None, act=None, prefix='ff'):
pre_state, pre_action = act[:, :options['act_hdim']], act[:, options['act_hdim']:]
# hidden = get_layer('lngru')[1](tparams, concatenate([h1, ctx, pre_action], axis=1),
# options, prefix=prefix + '_in',
# one_step=True, _init_state=pre_state)[0]
hidden = get_layer('gru')[1](tparams, concatenate([h1, ctx, pre_action], axis=1),
options, prefix=prefix + '_in',
one_step=True, _init_state=pre_state)[0]
output = get_layer('ff')[1](tparams, hidden,
options, prefix=prefix + '_out', activ='tanh')
# gate = get_layer('ff')[1](tparams, concatenate([hidden, output], axis=1), options, prefix=prefix + '_gate', activ='sigmoid')[:, 0]
# gate = get_layer('ff')[1](tparams, concatenate([h1, ctx, pre_action, output], axis=1), options, prefix=prefix + '_gate', activ='sigmoid')[:, 0]
# action = output * gate[:, None]
gate = get_layer('ff')[1](tparams, concatenate([h1, ctx, pre_action, output], axis=1), options, prefix=prefix + '_gate', activ='sigmoid')
action = output * gate
cur_act = concatenate([hidden, action], axis=1)
return action, cur_act