def __init__(self, game, paramOnly=False, nReps=1, record=False):
"""Initializes task environment
Args:
game - (string) - dict key of task to be solved (see domain/config.py)
Optional:
paramOnly - (bool) - only load parameters instead of launching task?
nReps - (nReps) - number of trials to get average fitness
"""
# Network properties
self.nInput = game.input_size
self.nOutput = game.output_size
self.actRange = game.h_act
self.absWCap = game.weightCap
self.layers = game.layers
self.activations = np.r_[np.full(1, 1), game.i_act, game.o_act]
# Environment
self.nReps = nReps
self.maxEpisodeLength = game.max_episode_length
self.actSelect = game.actionSelect
if not paramOnly:
if record:
env_to_wrap = make_env(game.env_name)
self.env = Monitor(env_to_wrap, "trial_recording/", force=True)
else:
self.env = make_env(game.env_name)
# Special needs...
self.needsClosed = (game.env_name.startswith("CartPoleSwingUp"))
def __init__(self, game, encoder, max_features, paramOnly=False, nReps=1):
"""Initializes task environment
Args:
game - (string) - dict key of task to be solved (see domain/config.py)
Optional:
paramOnly - (bool) - only load parameters instead of launching task?
nReps - (nReps) - number of trials to get average fitness
"""
# Network properties
if encoder == 'ascii' or encoder == 'count':
self.nInput = max_features
i_act = np.full(max_features, 1)
elif encoder == 'lstm':
self.nInput = 4096
i_act = np.full(4096, 1)
else:
self.nInput = game.input_size
i_act = game.i_act
self.nOutput = game.output_size
self.actRange = game.h_act
self.absWCap = game.weightCap
self.layers = game.layers
self.activations = np.r_[np.full(1, 1), i_act, game.o_act]
# Environment
self.nReps = nReps
self.maxEpisodeLength = game.max_episode_length
self.actSelect = game.actionSelect
if not paramOnly:
self.env = make_env(game.env_name, encoder, max_features)
# Special needs...
self.needsClosed = (game.env_name.startswith("CartPoleSwingUp"))