109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team.
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# All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass, field
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from typing import Any
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@dataclass
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class SACConfig:
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input_shapes: dict[str, list[int]] = field(
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default_factory=lambda: {
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"observation.image": [3, 84, 84],
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"observation.state": [4],
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}
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)
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output_shapes: dict[str, list[int]] = field(
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default_factory=lambda: {
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"action": [2],
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}
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)
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input_normalization_modes: dict[str, str] = field(
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default_factory=lambda: {
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"observation.image": "mean_std",
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"observation.state": "min_max",
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"observation.environment_state": "min_max",
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}
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)
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input_normalization_params: dict[str, dict[str, list[float]]] = field(
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default_factory=lambda: {
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"observation.image": {
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"mean": [[0.485, 0.456, 0.406]],
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"std": [[0.229, 0.224, 0.225]],
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},
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"observation.state": {"min": [-1, -1, -1, -1], "max": [1, 1, 1, 1]},
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}
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)
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output_normalization_modes: dict[str, str] = field(
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default_factory=lambda: {"action": "min_max"}
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)
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output_normalization_params: dict[str, dict[str, list[float]]] = field(
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default_factory=lambda: {
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"action": {"min": [-1, -1], "max": [1, 1]},
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}
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)
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# TODO: Move it outside of the config
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actor_learner_config: dict[str, str | int] = field(
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default_factory=lambda: {
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"learner_host": "127.0.0.1",
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"learner_port": 50051,
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}
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)
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camera_number: int = 1
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storage_device: str = "cpu"
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# Add type annotations for these fields:
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vision_encoder_name: str | None = field(default="helper2424/resnet10")
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freeze_vision_encoder: bool = True
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image_encoder_hidden_dim: int = 32
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shared_encoder: bool = True
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discount: float = 0.99
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temperature_init: float = 1.0
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num_critics: int = 2
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num_subsample_critics: int | None = None
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critic_lr: float = 3e-4
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actor_lr: float = 3e-4
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temperature_lr: float = 3e-4
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critic_target_update_weight: float = 0.005
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utd_ratio: int = 1 # If you want enable utd_ratio, you need to set it to >1
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state_encoder_hidden_dim: int = 256
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latent_dim: int = 256
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target_entropy: float | None = None
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use_backup_entropy: bool = True
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grad_clip_norm: float = 40.0
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critic_network_kwargs: dict[str, Any] = field(
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default_factory=lambda: {
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"hidden_dims": [256, 256],
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"activate_final": True,
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"final_activation": None,
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}
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)
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actor_network_kwargs: dict[str, Any] = field(
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default_factory=lambda: {
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"hidden_dims": [256, 256],
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"activate_final": True,
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}
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)
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policy_kwargs: dict[str, Any] = field(
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default_factory=lambda: {
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"use_tanh_squash": True,
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"log_std_min": -5,
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"log_std_max": 2,
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"init_final": 0.005,
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}
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)
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