feat: start of debug setup, experiment description,..

configs/hpc/debug.yaml

@@ -0,0 +1,13 @@
+# Configuration for debug session
+exp_name: "debug-experiment-10042026" # started on april 10
+seed: 42
+track: true
+wandb_project_name: "Let's-find-that-bug"
+wandb_entity: "SEL3-2026-Groep-4"
+
+num_envs: 32
+num_steps: 32
+total_timesteps: 102400
+
+cuda: true
+

experiments/debug-experiment-10042026/used_variables.md

@@ -0,0 +1,82 @@
+## Default envconfig
+task: Task = Task.DIRECTED_LOCOMOTION
+simulation_time: float = 5.0
+num_physics_steps_per_control_step: int = 10
+time_scale: int = 2
+camera_ids: list[int] = field(default_factory=lambda: [0, 1])
+render_size: tuple[int, int] = (480, 640)
+joint_randomization_noise_scale: float = 0.0
+target_distance: float = 3.0
+light_perlin_noise_scale: int = 0
+
+
+## Default ppoargs
+seed: int = 1
+torch_deterministic: bool = True
+cuda: bool = True
+track: bool = False
+checkpoint_frequency: int = 100
+learning_rate: float = 2.5e-4
+anneal_lr: bool = True
+gamma: float = 0.99
+gae_lambda: float = 0.95
+num_minibatches: int = 4
+update_epochs: int = 4
+norm_adv: bool = True
+clip_coef: float = 0.1
+clip_vloss: bool = True
+ent_coef: float = 0.01
+vf_coef: float = 0.5
+max_grad_norm: float = 0.5
+target_kl: float | None = None
+batch_size: int = 0
+minibatch_size: int = 0
+num_iterations: int = 0
+
+## Used config file:
+num_envs: 32
+num_steps: 32
+total_timesteps: 102400
+
+## Arena config:
+size: tuple[float, float] = (10.0, 5.0)
+sand_ground_color: bool = True
+attach_target: bool = True
+wall_height: float = 1.5
+wall_thickness: float = 0.1
+
+## Morphology:
+num_arms: int = 5
+num_segments_per_arm: int = 4
+use_p_control: bool = True
+use_torque_control: bool = False
+
+## MLPs:
+### Sensor & Feature_extractor:
+class GenericDenseLayersWithActivation(nn.Module):
+    layer_sizes: Sequence[int] = field(default_factory=lambda: [64, 64])
+    activation: Callable = nn.tanh
+
+    @nn.compact
+    def __call__(self, x):
+        for size in self.layer_sizes:
+            x = nn.Dense(size, kernel_init=orthogonal(jnp.sqrt(2)))(x)
+            x = self.activation(x)
+        return x
+
+### Actor:
+class Actor(nn.Module):
+    action_dim: int
+    @nn.compact
+    def __call__(self, x):
+        mean = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(x)
+        log_std = self.param("log_std", nn.initializers.zeros, (self.action_dim,))
+        return mean, log_std
+
+### Critic:
+class OneDenseLayerMLP(nn.Module):
+    @nn.compact
+    def __call__(self, x):
+        return nn.Dense(1, kernel_init=orthogonal(1), bias_init=constant(0.0))(x)
+
+### Observations:

scripts/train.py

@@ -68,7 +68,19 @@ if __name__ == "__main__":
     print_config(args, title="PPO Training Configuration")
 
     env = make_env(args.env_config_path, args.num_envs)
-
+    raw_env = env.raw
+    print(
+        "\n\n\n Observation space \n",
+        raw_env.observation_space,
+        "Action space \n",
+        raw_env.action_space,
+    )
+    print(
+        "\n\n\n Observation space \n",
+        raw_env.observation_space,
+        "Action space \n",
+        raw_env.action_space,
+    )
     torch.backends.cudnn.deterministic = args.torch_deterministic
 
     ppo_trainer = PPOTrainer(args, env, run_dir, run_name)