82 lines
2.1 KiB
Markdown
82 lines
2.1 KiB
Markdown
## Default envconfig
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task: Task = Task.DIRECTED_LOCOMOTION
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simulation_time: float = 5.0
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num_physics_steps_per_control_step: int = 10
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time_scale: int = 2
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camera_ids: list[int] = field(default_factory=lambda: [0, 1])
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render_size: tuple[int, int] = (480, 640)
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joint_randomization_noise_scale: float = 0.0
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target_distance: float = 3.0
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light_perlin_noise_scale: int = 0
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## Default ppoargs
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seed: int = 1
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torch_deterministic: bool = True
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cuda: bool = True
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track: bool = False
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checkpoint_frequency: int = 100
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learning_rate: float = 2.5e-4
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anneal_lr: bool = True
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gamma: float = 0.99
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gae_lambda: float = 0.95
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num_minibatches: int = 4
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update_epochs: int = 4
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norm_adv: bool = True
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clip_coef: float = 0.1
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clip_vloss: bool = True
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ent_coef: float = 0.01
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vf_coef: float = 0.5
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max_grad_norm: float = 0.5
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target_kl: float | None = None
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batch_size: int = 0
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minibatch_size: int = 0
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num_iterations: int = 0
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## Used config file:
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num_envs: 32
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num_steps: 32
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total_timesteps: 102400
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## Arena config:
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size: tuple[float, float] = (10.0, 5.0)
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sand_ground_color: bool = True
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attach_target: bool = True
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wall_height: float = 1.5
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wall_thickness: float = 0.1
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## Morphology:
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num_arms: int = 2
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num_segments_per_arm: int = 4
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use_p_control: bool = True
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use_torque_control: bool = False
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## MLPs:
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### Sensor & Feature_extractor:
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class GenericDenseLayersWithActivation(nn.Module):
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layer_sizes: Sequence[int] = field(default_factory=lambda: [64, 64])
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activation: Callable = nn.tanh
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@nn.compact
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def __call__(self, x):
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for size in self.layer_sizes:
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x = nn.Dense(size, kernel_init=orthogonal(jnp.sqrt(2)))(x)
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x = self.activation(x)
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return x
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### Actor:
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class Actor(nn.Module):
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action_dim: int
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@nn.compact
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def __call__(self, x):
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mean = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(x)
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log_std = self.param("log_std", nn.initializers.zeros, (self.action_dim,))
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return mean, log_std
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### Critic:
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class OneDenseLayerMLP(nn.Module):
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@nn.compact
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def __call__(self, x):
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return nn.Dense(1, kernel_init=orthogonal(1), bias_init=constant(0.0))(x)
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### Observations:
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