Audit Priority: P2
Audit Type: DOCS
Problem / Opportunity
PolyVision's README explains the current Phase 1 research system in substantial technical detail, but the repository landing page does not show the actual task, policy-visible state, human interaction surface, or representative evaluation output. For a reinforcement-learning project built around a game environment, this makes the first impression unusually abstract: a new reader can understand the architecture diagram and benchmark numbers without seeing what an episode or decision state actually looks like.
Why This Matters
The project now has a mature, tightly specified Phase 1 environment, a human-information-parity interface, frozen reference results, and rendering support. A compact visual walkthrough would make those implemented capabilities much easier to understand and evaluate without requiring readers to reconstruct the experience from the Java/Python architecture and several documentation pages. This is particularly useful for showcasing the distinction between the constrained Turn-10 research task and full-game Polytopia.
Evidence
README.md contains a Mermaid architecture diagram and extensive text, but no screenshot, GIF, episode visualization, or representative rendered decision state.pol_env/Tribes/py/gym_env.py already supports ansi, rgb_image/rgb_array, and Java GUI rendering modes, so the repository has an existing path to visualizing environment state.tools/human_policy_interface.py and tools/human_benchmark.py implement the policy-visible human interaction workflow used for information-parity benchmarking.README.md already reports the frozen v5 reference benchmark and links detailed evaluation artifacts, but does not visually connect those results to the task being evaluated.- No current open issue substantially represents repository presentation or a visual project walkthrough.
Proposed Outcome
Add a concise visual Phase 1 walkthrough near the top of the README using repository-owned/generated assets that show the current implemented task rather than an aspirational full-game system. The walkthrough should make it immediately clear what the agent sees/does, what the Turn-10 objective is, and what current evaluation output looks like.
Definition of Done
Constraints / Non-Goals
This is a presentation/documentation improvement, not a request to build a new renderer, redesign the human benchmark UI, change the research protocol, or expand the Phase 1 curriculum. Prefer current repository rendering/interface capabilities and existing frozen evidence over bespoke visualization infrastructure.
Validation
Review the repository landing page as a first-time reader and verify that the current task, interaction model, and benchmark result can be understood from the opening sections without first navigating into implementation files or long-form research documents.
Audit Priority: P2
Audit Type: DOCS
Problem / Opportunity
PolyVision's README explains the current Phase 1 research system in substantial technical detail, but the repository landing page does not show the actual task, policy-visible state, human interaction surface, or representative evaluation output. For a reinforcement-learning project built around a game environment, this makes the first impression unusually abstract: a new reader can understand the architecture diagram and benchmark numbers without seeing what an episode or decision state actually looks like.
Why This Matters
The project now has a mature, tightly specified Phase 1 environment, a human-information-parity interface, frozen reference results, and rendering support. A compact visual walkthrough would make those implemented capabilities much easier to understand and evaluate without requiring readers to reconstruct the experience from the Java/Python architecture and several documentation pages. This is particularly useful for showcasing the distinction between the constrained Turn-10 research task and full-game Polytopia.
Evidence
README.mdcontains a Mermaid architecture diagram and extensive text, but no screenshot, GIF, episode visualization, or representative rendered decision state.pol_env/Tribes/py/gym_env.pyalready supportsansi,rgb_image/rgb_array, and Java GUI rendering modes, so the repository has an existing path to visualizing environment state.tools/human_policy_interface.pyandtools/human_benchmark.pyimplement the policy-visible human interaction workflow used for information-parity benchmarking.README.mdalready reports the frozen v5 reference benchmark and links detailed evaluation artifacts, but does not visually connect those results to the task being evaluated.Proposed Outcome
Add a concise visual Phase 1 walkthrough near the top of the README using repository-owned/generated assets that show the current implemented task rather than an aspirational full-game system. The walkthrough should make it immediately clear what the agent sees/does, what the Turn-10 objective is, and what current evaluation output looks like.
Definition of Done
Constraints / Non-Goals
This is a presentation/documentation improvement, not a request to build a new renderer, redesign the human benchmark UI, change the research protocol, or expand the Phase 1 curriculum. Prefer current repository rendering/interface capabilities and existing frozen evidence over bespoke visualization infrastructure.
Validation
Review the repository landing page as a first-time reader and verify that the current task, interaction model, and benchmark result can be understood from the opening sections without first navigating into implementation files or long-form research documents.