fix: update file link
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@ -2,11 +2,11 @@
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## Design & architecture ([`/design`](./design/))
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- [Actor/critic architecture](./design/actor-critic.md): Description of the actor-critic pipeline.
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- [Communication](./design/communication.md): Message propagation, Nerve-Net style.
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- [Controllers](./design/controllers.md): Macroscopig brain toplogy, centralized, arm-level, segment-level.
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- [Input/output](./design/input_action_spaces.md): Description of the model's input and output.
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- [Learning algorithm](./design/learning_algorithm.md): RL techniques, i.e. PPO.
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- [MLP architecture](./design/mlp_architecture.md): Module wiring, state/action spaces, and actor-critic pipeline.
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- [Reward function](./design/learning_algorithm.md): Goals, fitness tracking, and reward structures.
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## API reference ([`/api`](./api/))
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@ -20,7 +20,6 @@ This pipeline treats the agent as a single entity and uses standard Proximal Pol
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Our policy and value networks use separate input networks/feature extractors as advised by the SEL3 course assistants and the blog. For continuous actions this should allow better learning at a small cost.
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```mermaid
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%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%%
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graph TD
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Obs([Global Observation])
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@ -69,7 +68,6 @@ critic for all nodes at once, for the following reasons:
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mathematically equivalent.
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```mermaid
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%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%%
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graph TD
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Obs([Local Observation])
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@ -96,7 +94,6 @@ graph TD
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Crit --> OutCrit
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Prop -.->|"message passing"|Prop
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```
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## Implementation Details (Network Depth)
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