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@ -102,7 +102,7 @@
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@ -1312,6 +1066,8 @@
<span class="w"> </span>evaluation.eval_max_steps<span class="o">=</span><span class="m">2000</span> <span class="w"> </span>evaluation.eval_max_steps<span class="o">=</span><span class="m">2000</span>
</code></pre></div> </code></pre></div>
<p>This script scans the <code>checkpoints/</code> directory of the specified run and evaluates every <code>.flax</code> file it finds using the model's training morphology.</p> <p>This script scans the <code>checkpoints/</code> directory of the specified run and evaluates every <code>.flax</code> file it finds using the model's training morphology.</p>
<hr />
<p>For a step-by-step walkthrough on using these evaluation phases to reproduce our project results, see the <strong><a href="../reproduction/">Results &amp; Reproduction Guide</a></strong>.</p>

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<h1 id="simulation-evaluation">Simulation &amp; Evaluation</h1> <h1 id="interactive-simulation-visualization">Interactive Simulation &amp; Visualization</h1>
<p>The simulation pipeline allows you to visualize trained models and evaluate their performance under various conditions.</p> <p>The simulation pipeline allows you to visualize trained models and observe their behavior under various conditions.</p>
<h2 id="overview">Overview</h2> <h2 id="overview">Overview</h2>
<p>The simulation pipeline is metadata-driven. Training-specific configurations (morphology, arena, environment, etc.) are automatically loaded from the <code>_metadata.yaml</code> file associated with the model checkpoint.</p> <p>The simulation pipeline is metadata-driven. Training-specific configurations (morphology, arena, environment, etc.) are automatically loaded from the <code>_metadata.yaml</code> file associated with the model checkpoint.</p>
<h2 id="basic-simulation">Basic Simulation</h2> <h2 id="basic-simulation">Basic Simulation</h2>
@ -1220,7 +974,7 @@
</code></pre></div> </code></pre></div>
<p>Videos and evaluation metadata are stored in timestamped folders alongside the model: <p>Videos and evaluation metadata are stored in timestamped folders alongside the model:
<code>runs/your_run/final_model_evaluations/eval_&lt;timestamp&gt;/simulation.mp4</code></p> <code>runs/your_run/final_model_evaluations/eval_&lt;timestamp&gt;/simulation.mp4</code></p>
<p>For batch evaluation and cross-model comparison, see the <strong><a href="../evaluation/">Evaluation Guide</a></strong>.</p> <p>For batch evaluation, checkpoint analysis, and cross-model architecture comparisons, see the <strong><a href="../evaluation/">Checkpoint &amp; Model Evaluation Guide</a></strong>.</p>

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@ -1275,6 +1051,8 @@
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/analysis/explore_tensorboard.py<span class="w"> </span>runs/your_run_name/ <div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/analysis/explore_tensorboard.py<span class="w"> </span>runs/your_run_name/
</code></pre></div> </code></pre></div>
<p>See the detailed description in <a href="../../scripts/analysis/"><code>/scripts/analysis/README.md</code></a>.</p> <p>See the detailed description in <a href="../../scripts/analysis/"><code>/scripts/analysis/README.md</code></a>.</p>
<h2 id="developer-logging-api">Developer Logging API</h2>
<p>For details on the developer API of our internal logging library (how backend routing, checkpoint synchronization, and singleton initialization works), see the <strong><a href="../../src/experiment_logger/">Experiment Logger API Guide</a></strong>.</p>

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@ -1269,6 +1023,7 @@
<p>This guide covers how to configure and run training experiments for the Brittle Star project using Hydra-based configurations.</p> <p>This guide covers how to configure and run training experiments for the Brittle Star project using Hydra-based configurations.</p>
<h2 id="configuration">Configuration</h2> <h2 id="configuration">Configuration</h2>
<p>The project uses a modular configuration system powered by <a href="https://hydra.cc/">Hydra</a>. Instead of passing many command-line flags, you select and override configuration groups.</p> <p>The project uses a modular configuration system powered by <a href="https://hydra.cc/">Hydra</a>. Instead of passing many command-line flags, you select and override configuration groups.</p>
<p>For a detailed guide on the structure, validation, and usage of our Hydra configuration files, see the <strong><a href="../../configs/">Brittle Star Configuration System Guide</a></strong>.</p>
<h3 id="creating-a-custom-experiment">Creating a Custom Experiment</h3> <h3 id="creating-a-custom-experiment">Creating a Custom Experiment</h3>
<ol> <ol>
<li> <li>
@ -1301,31 +1056,9 @@
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/train.py<span class="w"> </span>evaluation.evaluate_checkpoints<span class="o">=</span><span class="nb">true</span> <div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/train.py<span class="w"> </span>evaluation.evaluate_checkpoints<span class="o">=</span><span class="nb">true</span>
</code></pre></div> </code></pre></div>
<h2 id="reproducing-experiments">Reproducing Experiments</h2> <h2 id="reproducing-experiments">Reproducing Experiments</h2>
<p>To ensure scientific validity and allow other researchers to reproduce your training runs, follow these steps:</p> <p>For detailed steps on how to reproduce training runs, locate run configuration metadata, or reproduce our experiments using Weights &amp; Biases (WandB), see the <strong><a href="../reproduction/">Results &amp; Reproduction Guide</a></strong>.</p>
<ol>
<li>
<p><strong>Lock Environment Dependencies</strong>:
Always use the exact environment lockfile when running experiments. Run:
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>sync<span class="w"> </span>--frozen
</code></pre></div>
This guarantees that the same package versions (including JAX, Flax, and MuJoCo) are used.</p>
</li>
<li>
<p><strong>Save and Locate Configuration Metadata</strong>:
Every time you start a training run, the configuration is fully resolved by Hydra and saved as a metadata YAML file:</p>
</li>
<li>For checkpointed steps: <code>runs/&lt;run_dir&gt;/checkpoints/&lt;prefix&gt;_step_&lt;step&gt;_metadata.yaml</code></li>
<li>For the final model: <code>runs/&lt;run_dir&gt;/final_model_metadata.yaml</code></li>
</ol>
<p>This metadata file contains every active hyperparameter (e.g., learning rate, morphology configuration, PPO parameters, etc.) for that specific run.</p>
<ol>
<li><strong>Re-Run with Pinning</strong>:
To reproduce a run, execute the training script with the configuration parameters specified in the metadata file, making sure to reuse the same seed:
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/train.py<span class="w"> </span><span class="nv">experiment</span><span class="o">=</span>my_experiment<span class="w"> </span>ppo.learning_rate<span class="o">=</span><span class="m">0</span>.001<span class="w"> </span>experiment.seed<span class="o">=</span><span class="m">42</span>
</code></pre></div></li>
</ol>
<hr /> <hr />
<p>For more details on evaluation metrics and comparison tools, see <a href="../evaluation/">Evaluation</a>.</p> <p>For more details on evaluation metrics and comparison tools, see <a href="../evaluation/">Checkpoint &amp; Model Evaluation</a>.</p>
<p>For more details on tracking your experiments, see <a href="../tracking/">Tracking &amp; Monitoring</a>.</p> <p>For more details on tracking your experiments, see <a href="../tracking/">Tracking &amp; Monitoring</a>.</p>

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# Centralized Actor-Critic Architecture
# Baseline configuration with a single global sensor and motor.
# Default values are defined in CentralizedConfig dataclass.
# Use this configuration for standard PPO experiments.
name: "centralized"
sensor:
hidden_dims: [300, 300, 300]
activation: "tanh"
motor:
hidden_dims: []
activation: "tanh"
feature_extractor:
hidden_dims: [300, 300, 300]
activation: "tanh"
critic:
hidden_dims: []
activation: "tanh"

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@ -0,0 +1,32 @@
# Decentralized Actor Architecture (NerveNet-MLP variant)
# Multi-agent/distributed configuration using local sensors, propagators, and motors.
# Default values are defined in DecentralizedConfig dataclass.
# Use this configuration for decentralized execution experiments.
name: "decentralized"
sensor:
hidden_dims: [300, 300, 300]
activation: "tanh"
propagator:
hidden_dims: [300, 300, 300]
activation: "tanh"
motor:
hidden_dims: []
activation: "tanh"
feature_extractor:
hidden_dims: [300, 300, 300]
activation: "tanh"
critic:
hidden_dims: []
activation: "tanh"
# Synchronous message-passing rounds per control step
message_passing_steps: 4
# Connectivity topology (e.g., ring, fully_connected)
topology_type: "fully_connected"

8
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@ -0,0 +1,8 @@
# Default Arena Configuration
# Base aquarium environment settings.
size: [10.0, 5.0]
sand_ground_color: true
attach_target: true
wall_height: 1.5
wall_thickness: 0.1

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@ -0,0 +1,71 @@
# Custom Main Configuration
#
# Use with:
# uv run python scripts/train.py --config-name main_config_custom
#
# This keeps the project defaults intact while giving you a single custom
# training entrypoint you can edit freely.
defaults:
- brittle_star_config
- experiment: base
- logging: default
- evaluation: default
- ppo: default
- architecture: centralized
- morphology: 5_arms_full
- arena: default
- environment: directed_locomotion
- simulation: default
- _self_
morphology:
morph_mode: CENTRALIZED
experiment:
exp_name: "final-models/centralized/"
seed: 42
torch_deterministic: true
cuda: true
logging:
track: true
save_model: true
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 20
wandb_project_name: "final-models"
evaluation:
evaluate_checkpoints: true
eval_max_steps: 2000
eval_seed: 0
ppo:
learning_rate: 0.0001
total_timesteps: 16384000
num_envs: 128
num_steps: 64
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 32
update_epochs: 4
norm_adv: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.001
vf_coef: 1.0
max_grad_norm: 0.5
target_kl: 0.02
environment:
simulation_time: 100000.0
target_distance: 3.0
hydra:
job:
chdir: true
run:
dir: ${experiment.base_run_dir}/${experiment.exp_name}/${now:%Y-%m-%d}/${now:%H-%M-%S}

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@ -0,0 +1,2 @@
simulation_time: 50000.0
target_distance: 3.0

12
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@ -0,0 +1,12 @@
# Directed Locomotion Environment
# Baseline task setting.
task: DIRECTED_LOCOMOTION
simulation_time: 100000.0
num_physics_steps_per_control_step: 10
time_scale: 2
camera_ids: [0, 1]
render_size: [480, 640]
joint_randomization_noise_scale: 0.0
target_distance: 3.0
light_perlin_noise_scale: 0

12
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@ -0,0 +1,12 @@
# Light Escape Environment
# Advanced task requiring movement away from light source.
task: LIGHT_ESCAPE
simulation_time: 100000.0
num_physics_steps_per_control_step: 10
time_scale: 2
camera_ids: [0, 1]
render_size: [480, 640]
joint_randomization_noise_scale: 0.0
target_distance: 3.0
light_perlin_noise_scale: 200 # Must be integer factor of 200

8
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@ -0,0 +1,8 @@
# Default Evaluation Configuration
# Settings used for checkpoint evaluation during training.
evaluate_checkpoints: false
# Max number of control steps during evaluation rollout.
eval_max_steps: 2000
# Seed for deterministic evaluation reset.
eval_seed: 0

23
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@ -0,0 +1,23 @@
# @package evaluation
# Configuration for the models used in the poster comparison.
# Standard evaluation settings
evaluate_checkpoints: false
eval_max_steps: 5000
eval_seed: 0
# Cross-model comparison settings
# We use 10 episodes to get a more robust average for the final poster results.
comparison_base_seed: 0
comparison_num_episodes: 2
comparison_output_csv: "runs/evaluation/comparison.csv"
# Paths to the .cleanrl_model files to be compared (relative to workspace root).
comparison_models:
- "runs/input-space-2-arms/2026-05-02/08-14-58/final_model.flax"
# Path to the morphologies to evaluate against.
comparison_morphologies:
- "configs/morphology/5_arms_full.yaml"
- "configs/morphology/3_arms.yaml"
- "configs/morphology/2_arms.yaml"

7
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@ -0,0 +1,7 @@
# Base Experiment Configuration
# Default values align with ExperimentConfig dataclass.
exp_name: "brittle_star_ppo"
seed: 1
torch_deterministic: true
cuda: true

7
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@ -0,0 +1,7 @@
# Testing Experiment Configuration
# Quick experiment for local development/testing.
exp_name: "dev_test_brittle_star"
seed: 42
torch_deterministic: true
cuda: true

7
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@ -0,0 +1,7 @@
# HPC Smoke Test Configuration
# Uses minimal settings but simulates HPC environment.
exp_name: "hpc_smoke_test"
seed: 123
torch_deterministic: true
cuda: true

6
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@ -0,0 +1,6 @@
# Testing chicken dinner 4 but further distance.
exp_name: "long2arm"
seed: 123
torch_deterministic: true
cuda: true

74
configs/fully-connected-final.yaml Normal file
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@ -0,0 +1,74 @@
# Custom Main Configuration
#
# Use with:
# uv run python scripts/train.py --config-name main_config_custom
#
# This keeps the project defaults intact while giving you a single custom
# training entrypoint you can edit freely.
defaults:
- brittle_star_config
- experiment: base
- logging: default
- evaluation: default
- ppo: default
- architecture: decentralized
- morphology: 5_arms_full
- arena: default
- environment: directed_locomotion
- simulation: default
- _self_
architecture:
topology_type: "fully_connected"
morphology:
morph_mode: FULLY_CONNECTED
experiment:
exp_name: "final-models/fully-connected/"
seed: 42
torch_deterministic: true
cuda: true
logging:
track: true
save_model: true
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 20
wandb_project_name: "final-models"
evaluation:
evaluate_checkpoints: true
eval_max_steps: 2000
eval_seed: 0
ppo:
learning_rate: 0.0001
total_timesteps: 16384000
num_envs: 128
num_steps: 64
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 32
update_epochs: 4
norm_adv: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.001
vf_coef: 1.0
max_grad_norm: 0.5
target_kl: 0.02
environment:
simulation_time: 100000.0
target_distance: 3.0
hydra:
job:
chdir: true
run:
dir: ${experiment.base_run_dir}/${experiment.exp_name}/${now:%Y-%m-%d}/${now:%H-%M-%S}

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configs/index.html Normal file
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13
configs/logging/default.yaml Normal file
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@ -0,0 +1,13 @@
# Default Logging Configuration
# Offline local-only setup (WandB disabled).
track: false
wandb_project_name: "PPO-Modularity"
wandb_entity: "SEL3-2026-Groep-4"
capture_video: false
save_model: true
save_checkpoints: true
checkpoint_frequency: 100
upload_final_model: false
upload_checkpoints: false
hf_entity: ""

9
configs/logging/hpc.yaml Normal file
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@ -0,0 +1,9 @@
track: true
wandb_project_name: "hpc-default"
wandb_entity: "SEL3-2026-Groep-4"
save_model: true
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 100
hf_entity: ""

13
configs/logging/wandb_enabled.yaml Normal file
View file

@ -0,0 +1,13 @@
# WandB Enabled Logging Configuration
# For production/cloud experiments with weights synced.
track: true
wandb_project_name: "default-project"
wandb_entity: "SEL3-2026-Groep-4"
capture_video: false
save_model: true
save_checkpoints: true
checkpoint_frequency: 100
upload_final_model: true
upload_checkpoints: false
hf_entity: ""

23
configs/main_config.yaml Normal file
View file

@ -0,0 +1,23 @@
# Brittle Star Project - Main Configuration
# This file defines the default composition of the hierarchical configuration.
# Sub-configs are loaded from the relative directories.
defaults:
- brittle_star_config
- experiment: base
- logging: default
- evaluation: default
- ppo: default
- architecture: centralized
- morphology: 5_arms_full
- arena: default
- environment: directed_locomotion
- obs_bounds: default
- simulation: default
- _self_
hydra:
job:
chdir: True
run:
dir: ${experiment.base_run_dir}/${experiment.exp_name}/${now:%Y-%m-%d}/${now:%H-%M-%S}

5
configs/morphology/2_arms.yaml Normal file
View file

@ -0,0 +1,5 @@
# 2 Arms Morphology Configuration
segments_per_arm: [4, 0, 4, 0, 0]
use_p_control: true
use_torque_control: false

6
configs/morphology/2_arms_decentralized.yaml Normal file
View file

@ -0,0 +1,6 @@
# 2 Arms Morphology Configuration
segments_per_arm: [4, 0, 4, 0, 0]
use_p_control: true
use_torque_control: false
morph_mode: FULLY_CONNECTED

6
configs/morphology/3_arms.yaml Normal file
View file

@ -0,0 +1,6 @@
# 3 Arms Morphology Configuration
# Symmetric amputation (arms 1 and 3 removed).
segments_per_arm: [4, 0, 4, 0, 4]
use_p_control: true
use_torque_control: false

6
configs/morphology/5_arms_damaged.yaml Normal file
View file

@ -0,0 +1,6 @@
# 5 Arms Full Morphology Configuration
# Baseline 5-arm brittle star.
segments_per_arm: [4, 4, 0, 4, 4]
use_p_control: true
use_torque_control: false

6
configs/morphology/5_arms_full.yaml Normal file
View file

@ -0,0 +1,6 @@
# 5 Arms Full Morphology Configuration
# Baseline 5-arm brittle star.
segments_per_arm: [4, 4, 4, 4, 4]
use_p_control: true
use_torque_control: false

7
configs/morphology/5_arms_full_fullconnected.yaml Normal file
View file

@ -0,0 +1,7 @@
# 5 Arms Full Morphology Configuration
# Baseline 5-arm brittle star.
segments_per_arm: [4, 4, 4, 4, 4]
use_p_control: true
use_torque_control: false
morph_mode: FULLY_CONNECTED

6
configs/morphology/partial_amputation.yaml Normal file
View file

@ -0,0 +1,6 @@
# Partial Amputation Configuration
# Random partial amputation for robustness testing.
segments_per_arm: [4, 2, 4, 4, 4]
use_p_control: true
use_torque_control: false

1
configs/obs_bounds/default.yaml Normal file
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@ -0,0 +1 @@
# Defaults provided by dataclass

16
configs/ppo/chickendinnerwinner.yaml Normal file
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@ -0,0 +1,16 @@
anneal_lr: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.001
gae_lambda: 0.95
gamma: 0.99
learning_rate: 0.0001
max_grad_norm: 0.5
norm_adv: true
num_envs: 32
num_minibatches: 32
num_steps: 64
target_kl: 0.02
total_timesteps: 12288000
update_epochs: 4
vf_coef: 1.0

16
configs/ppo/debug.yaml Normal file
View file

@ -0,0 +1,16 @@
learning_rate: 0.0003
total_timesteps: 409600
num_envs: 32
num_steps: 32
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 32
update_epochs: 4
norm_adv: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.005
vf_coef: 1.0
max_grad_norm: 0.5
target_kl: null

19
configs/ppo/default.yaml Normal file
View file

@ -0,0 +1,19 @@
# Default PPO Configuration
# Standard hyperparams from original codebase.
learning_rate: 0.00025
total_timesteps: 10000000
num_envs: 100
num_steps: 128
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 4
update_epochs: 4
norm_adv: true
clip_coef: 0.1
clip_vloss: true
ent_coef: 0.01
vf_coef: 0.5
max_grad_norm: 0.5
target_kl: null

16
configs/ppo/dev_larger_timesteps_larger_rolloutsteps.yaml Normal file
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@ -0,0 +1,16 @@
learning_rate: 0.0003
total_timesteps: 1228800
num_envs: 32
num_steps: 64
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 32
update_epochs: 4
norm_adv: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.005
vf_coef: 1.0
max_grad_norm: 0.5
target_kl: null

19
configs/ppo/smoke_test.yaml Normal file
View file

@ -0,0 +1,19 @@
# Fast PPO Configuration
# Lower timestep count for quick iterations/testing.
learning_rate: 0.0005
total_timesteps: 1024
num_envs: 32
num_steps: 32
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 4
update_epochs: 4
norm_adv: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.01
vf_coef: 0.5
max_grad_norm: 0.5
target_kl: null

19
configs/ppo/stable.yaml Normal file
View file

@ -0,0 +1,19 @@
# Stable PPO Configuration
# Standard hyperparams with lower LR and larger batch.
learning_rate: 0.0001
total_timesteps: 10000000
num_envs: 100
num_steps: 256
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 8
update_epochs: 4
norm_adv: true
clip_coef: 0.1
clip_vloss: true
ent_coef: 0.01
vf_coef: 0.5
max_grad_norm: 0.5
target_kl: null

74
configs/ring-final.yaml Normal file
View file

@ -0,0 +1,74 @@
# Custom Main Configuration
#
# Use with:
# uv run python scripts/train.py --config-name main_config_custom
#
# This keeps the project defaults intact while giving you a single custom
# training entrypoint you can edit freely.
defaults:
- brittle_star_config
- experiment: base
- logging: default
- evaluation: default
- ppo: default
- architecture: decentralized
- morphology: 5_arms_full
- arena: default
- environment: directed_locomotion
- simulation: default
- _self_
architecture:
topology_type: "ring"
morphology:
morph_mode: RING
experiment:
exp_name: "final-models/ring/"
seed: 42
torch_deterministic: true
cuda: true
logging:
track: true
save_model: true
save_checkpoints: true
upload_final_model: true
upload_checkpoints: true
checkpoint_frequency: 20
wandb_project_name: "final-models"
evaluation:
evaluate_checkpoints: true
eval_max_steps: 2000
eval_seed: 0
ppo:
learning_rate: 0.0001
total_timesteps: 16384000
num_envs: 128
num_steps: 64
anneal_lr: true
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 32
update_epochs: 4
norm_adv: true
clip_coef: 0.2
clip_vloss: true
ent_coef: 0.001
vf_coef: 1.0
max_grad_norm: 0.5
target_kl: 0.02
environment:
simulation_time: 100000.0
target_distance: 3.0
hydra:
job:
chdir: true
run:
dir: ${experiment.base_run_dir}/${experiment.exp_name}/${now:%Y-%m-%d}/${now:%H-%M-%S}

26
configs/simulation/default.yaml Normal file
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@ -0,0 +1,26 @@
# Default Simulation Settings
# These values are used by scripts/simulate.py
# Path to the trained model (optional)
model_path: null
# Script behavior
headless: false
# In headless mode this is required; in viewer mode null means "infinite".
max_steps: null
# Optional: override morphology for amputation experiments.
# Points to a morphology config YAML file (e.g. configs/morphology/3_arms.yaml).
# If null, the training morphology from the model's metadata is used.
morphology_override: null
# Video recording (requires [evaluation] extra)
record_video: false
# When null, video is saved in a per-model evaluation folder alongside the model.
video_output_path: null
# Camera ID to use for video recording (1 is usually the close-up camera)
camera_id: 1
# Optional override for the metadata YAML file path.
# If null, the script looks for `<model_name>_metadata.yaml` alongside the model_path.
metadata_path: null

720
design/actor-critic/index.html
View file

@ -9,7 +9,7 @@
<link rel="prev" href="../../api/training/"> <link rel="prev" href="../..">
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@ -205,7 +205,7 @@
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</span>
</a>
</li> </li>
@ -1200,7 +954,8 @@
<ul> <ul>
<li><strong><a href="DEVELOPMENT/">Installation Instructions</a></strong>: Steps to set up your development environment locally or in a devcontainer using <code>uv</code>, including GPU configuration. For High-Performance Computing (HPC) setup details, see the <strong><a href="HPC/">HPC Guide</a></strong>.</li> <li><strong><a href="DEVELOPMENT/">Installation Instructions</a></strong>: Steps to set up your development environment locally or in a devcontainer using <code>uv</code>, including GPU configuration. For High-Performance Computing (HPC) setup details, see the <strong><a href="HPC/">HPC Guide</a></strong>.</li>
<li><strong><a href="api/training/">How to Run Experiments</a></strong>: A complete guide on running training jobs, setting custom hyperparameters, and overriding config options using Hydra.</li> <li><strong><a href="api/training/">How to Run Experiments</a></strong>: A complete guide on running training jobs, setting custom hyperparameters, and overriding config options using Hydra.</li>
<li><strong><a href="api/training/#reproducing-experiments">Reproducing Experiments</a></strong>: Best practices for reproducing past training runs using exact seeds, dependencies, and automatic metadata logging.</li> <li><strong><a href="api/reproduction/">Results &amp; Reproduction</a></strong>: Guide on how to access our public WandB training runs table and reproduce our training and evaluation phases (determining the best checkpoint vs. comparing architectures).</li>
<li><strong><a href="CONTRIBUTING/">Contribution Guidelines</a></strong>: Standards, rules, and best practices for developing and adding code to the repository.</li>
<li><strong><a href="#repository-structure">Repository Structure</a></strong>: Overview of the directories and files within the codebase.</li> <li><strong><a href="#repository-structure">Repository Structure</a></strong>: Overview of the directories and files within the codebase.</li>
</ul> </ul>
<h2 id="repository-structure">Repository Structure</h2> <h2 id="repository-structure">Repository Structure</h2>
@ -1219,22 +974,23 @@
<h2 id="design-architecture-design">Design &amp; architecture (<code>/design</code>)</h2> <h2 id="design-architecture-design">Design &amp; architecture (<code>/design</code>)</h2>
<p>If you are interested in the "why did you do it like this?"</p> <p>If you are interested in the "why did you do it like this?"</p>
<ul> <ul>
<li><a href="design/actor-critic/">Actor/critic architecture</a>: Description of the actor-critic pipeline.</li> <li><a href="design/actor-critic/">Actor-Critic Architecture</a>: Description of the actor-critic pipeline.</li>
<li><a href="design/communication/">Communication</a>: Message propagation, Nerve-Net style.</li> <li><a href="design/communication/">Communication Scheme</a>: Message propagation, Nerve-Net style.</li>
<li><a href="design/controllers/">Controllers</a>: Macroscopig brain toplogy, centralized, arm-level, segment-level.</li> <li><a href="design/controllers/">Modularity &amp; Topology</a>: Macroscopic brain topology, centralized, arm-level, segment-level.</li>
<li><a href="design/input_action_spaces/">Input/output</a>: Description of the model's input and output.</li> <li><a href="design/input_action_spaces/">Input &amp; Action Spaces</a>: Description of the model's input and output.</li>
<li><a href="design/learning_algorithm/">Learning algorithm</a>: RL techniques, i.e. PPO.</li> <li><a href="design/learning_algorithm/">Reinforcement Learning Algorithm</a>: RL techniques, i.e. PPO.</li>
<li><a href="design/reward_function/">Reward function</a>: Goals, fitness tracking, and reward structures.</li> <li><a href="design/reward_function/">Reward Function &amp; Observation Space</a>: Goals, fitness tracking, and reward structures.</li>
</ul> </ul>
<h2 id="api-reference-api">API reference (<code>/api</code>)</h2> <h2 id="api-reference-api">API reference (<code>/api</code>)</h2>
<p>If you are interested in the "how do I use it?"</p> <p>If you are interested in the "how do I use it?"</p>
<ul> <ul>
<li><a href="api/training/">Training</a>: How to configure and run experiments.</li> <li><a href="api/environment/">Brittle Star Environment</a>: MuJoCo environment interaction and configuration.</li>
<li><a href="api/training/">Training Models</a>: How to configure and run experiments.</li>
<li><a href="api/tracking/">Tracking &amp; Monitoring</a>: Setting up WandB and TensorBoard to monitor runs.</li> <li><a href="api/tracking/">Tracking &amp; Monitoring</a>: Setting up WandB and TensorBoard to monitor runs.</li>
<li><a href="api/simulation/">Simulation</a>: Visualizing and evaluating models.</li> <li><a href="api/evaluation/">Checkpoint &amp; Model Evaluation</a>: Evaluating checkpoints and comparing fault tolerance.</li>
<li><a href="api/environment/">Environment</a>: MuJoCo environment interaction and configuration.</li> <li><a href="api/simulation/">Interactive Simulation &amp; Visualization</a>: Visualizing models in the MuJoCo viewer or rendering simulation videos.</li>
<li><a href="api/analysis/">Analysis</a>: Comparing checkpoints and generating plots.</li> <li><a href="api/analysis/">Analysis &amp; Plotting Tools</a>: Comparing checkpoints and generating plots.</li>
<li><a href="api/evaluation/">Evaluation</a>: Evaluating checkpoints and comparing fault tolerance.</li> <li><a href="api/reproduction/">Results &amp; Reproduction</a>: Accessing WandB results and running reproduction pipelines.</li>
</ul> </ul>

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