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| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| """Default configuration for agent and environment.""" | |
| from absl import logging | |
| from common import config_lib # brain coder | |
| def default_config(): | |
| return config_lib.Config( | |
| agent=config_lib.OneOf( | |
| [config_lib.Config( | |
| algorithm='pg', | |
| policy_lstm_sizes=[35,35], | |
| # Set value_lstm_sizes to None to share weights with policy. | |
| value_lstm_sizes=[35,35], | |
| obs_embedding_size=10, | |
| grad_clip_threshold=10.0, | |
| param_init_factor=1.0, | |
| lr=5e-5, | |
| pi_loss_hparam=1.0, | |
| vf_loss_hparam=0.5, | |
| entropy_beta=1e-2, | |
| regularizer=0.0, | |
| softmax_tr=1.0, # Reciprocal temperature. | |
| optimizer='rmsprop', # 'adam', 'sgd', 'rmsprop' | |
| topk=0, # Top-k unique codes will be stored. | |
| topk_loss_hparam=0.0, # off policy loss multiplier. | |
| # Uniformly sample this many episodes from topk buffer per batch. | |
| # If topk is 0, this has no effect. | |
| topk_batch_size=1, | |
| # Exponential moving average baseline for REINFORCE. | |
| # If zero, A2C is used. | |
| # If non-zero, should be close to 1, like .99, .999, etc. | |
| ema_baseline_decay=0.99, | |
| # Whether agent can emit EOS token. If true, agent can emit EOS | |
| # token which ends the episode early (ends the sequence). | |
| # If false, agent must emit tokens until the timestep limit is | |
| # reached. e.g. True means variable length code, False means fixed | |
| # length code. | |
| # WARNING: Making this false slows things down. | |
| eos_token=False, | |
| replay_temperature=1.0, | |
| # Replay probability. 1 = always replay, 0 = always on policy. | |
| alpha=0.0, | |
| # Whether to normalize importance weights in each minibatch. | |
| iw_normalize=True), | |
| config_lib.Config( | |
| algorithm='ga', | |
| crossover_rate=0.99, | |
| mutation_rate=0.086), | |
| config_lib.Config( | |
| algorithm='rand')], | |
| algorithm='pg', | |
| ), | |
| env=config_lib.Config( | |
| # If True, task-specific settings are not needed. | |
| task='', # 'print', 'echo', 'reverse', 'remove', ... | |
| task_cycle=[], # If non-empty, reptitions will cycle through tasks. | |
| task_kwargs='{}', # Python dict literal. | |
| task_manager_config=config_lib.Config( | |
| # Reward recieved per test case. These bonuses will be scaled | |
| # based on how many test cases there are. | |
| correct_bonus=2.0, # Bonus for code getting correct answer. | |
| code_length_bonus=1.0), # Maximum bonus for short code. | |
| correct_syntax=False, | |
| ), | |
| batch_size=64, | |
| timestep_limit=32) | |
| def default_config_with_updates(config_string, do_logging=True): | |
| if do_logging: | |
| logging.info('Config string: "%s"', config_string) | |
| config = default_config() | |
| config.strict_update(config_lib.Config.parse(config_string)) | |
| if do_logging: | |
| logging.info('Config:\n%s', config.pretty_str()) | |
| return config | |