def next_frame_basic_recurrent(): """Basic 2-frame recurrent model with stochastic tower.""" hparams = basic_stochastic.next_frame_basic_stochastic_discrete() hparams.video_num_input_frames = 4 hparams.video_num_target_frames = 4 hparams.add_hparam("num_lstm_layers", 2) hparams.add_hparam("num_lstm_filters", 256) return hparams
def next_frame_basic_recurrent(): """Basic 2-frame recurrent model with stochastic tower.""" hparams = basic_stochastic.next_frame_basic_stochastic_discrete() hparams.filter_double_steps = 2 hparams.hidden_size = 64 hparams.video_num_input_frames = 4 hparams.video_num_target_frames = 4 hparams.concat_internal_states = False hparams.add_hparam("num_lstm_layers", 2) hparams.add_hparam("num_lstm_filters", 256) return hparams
def next_frame_basic_recurrent(): """Basic 2-frame recurrent model with stochastic tower.""" hparams = basic_stochastic.next_frame_basic_stochastic_discrete() hparams.filter_double_steps = 2 hparams.hidden_size = 64 hparams.video_num_input_frames = 4 hparams.video_num_target_frames = 4 hparams.concat_internal_states = False hparams.add_hparam("num_lstm_layers", 2) hparams.add_hparam("num_lstm_filters", 256) return hparams
def ppo_original_world_model_stochastic_discrete(): """Atari parameters with stochastic discrete world model as policy.""" hparams = ppo_original_params() hparams.policy_network = "next_frame_basic_stochastic_discrete" hparams_keys = hparams.values().keys() video_hparams = basic_stochastic.next_frame_basic_stochastic_discrete() for (name, value) in six.iteritems(video_hparams.values()): if name in hparams_keys: hparams.set_hparam(name, value) else: hparams.add_hparam(name, value) # To avoid OOM. Probably way to small. hparams.optimization_batch_size = 1 return hparams