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# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Default SAC config values."""
import ml_collections
def get_config():
"""Returns default config."""
config = ml_collections.ConfigDict()
# ================================================= #
# Placeholders.
# ================================================= #
# These values will be filled at runtime once the gym.Env is loaded.
obs_dim = ml_collections.FieldReference(None, field_type=int)
action_dim = ml_collections.FieldReference(None, field_type=int)
action_range = ml_collections.FieldReference(None, field_type=tuple)
chunk_len = ml_collections.FieldReference(None, field_type=int)
# ================================================= #
# Main parameters.
# ================================================= #
config.save_dir = "./rl_runs/"
# Set this to True to allow CUDA to find the best convolutional algorithm to
# use for the given parameters. When False, cuDNN will deterministically
# select the same algorithm at a possible cost in performance.
config.cudnn_benchmark = True
# Enforce CUDA convolution determinism. The algorithm itself might not be
# deterministic so setting this to True ensures we make it repeatable.
config.cudnn_deterministic = True #False
# ================================================= #
# Wrappers.
# ================================================= #
config.action_repeat = 1
config.frame_stack = 1
config.reward_wrapper = ml_collections.ConfigDict()
config.reward_wrapper.pretrained_path = ""
# Can be one of ['distance_to_goal', 'goal_classifier'].
config.reward_wrapper.type = "distance_to_goal"
# ================================================= #
# Training parameters.
# ================================================= #
config.num_train_steps = 75_000
config.replay_buffer_capacity = 1_000_000
config.num_seed_steps = 20_000
config.num_eval_episodes = 10
config.eval_frequency = 20_000 #5_000
config.checkpoint_frequency = 50_000
config.log_frequency = 10_000
config.save_video = True
# ================================================= #
# SAC parameters.
# ================================================= #
config.sac = ml_collections.ConfigDict()
config.sac.obs_dim = obs_dim
config.sac.action_dim = action_dim
config.sac.action_range = action_range
config.sac.chunk_len = chunk_len
config.sac.discount = 0.99
config.sac.init_temperature = 0.1
config.sac.alpha_lr = 5e-5
config.sac.alpha_betas = [0.9, 0.999]
config.sac.actor_lr = 5e-5
config.sac.actor_betas = [0.9, 0.999]
config.sac.actor_update_frequency = 1
config.sac.critic_lr = 5e-5
config.sac.critic_betas = [0.9, 0.999]
config.sac.critic_tau = 0.005
config.sac.critic_target_update_frequency = 1
config.sac.batch_size = 256
config.sac.learnable_temperature = True
# ================================================= #
# Critic parameters.
# ================================================= #
config.sac.critic = ml_collections.ConfigDict()
config.sac.critic.obs_dim = obs_dim
config.sac.critic.action_dim = action_dim * chunk_len
config.sac.critic.hidden_dim = 1024
config.sac.critic.hidden_depth = 2
# ================================================= #
# Actor parameters.
# ================================================= #
config.sac.actor = ml_collections.ConfigDict()
config.sac.actor.obs_dim = obs_dim
config.sac.actor.action_dim = action_dim * chunk_len
config.sac.actor.hidden_dim = 1024
config.sac.actor.hidden_depth = 2
config.sac.actor.log_std_bounds = [-5, 1]
# ================================================= #
# Additional parameters.
# ================================================= #
config.epsilon = 0.21
config.threshold_for_vgen = 0.8
config.threshold_for_random_seq = 0.8
config.sample_per_seq = 8
return config
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