GameOfLife

A persistent simulated 3D voxel world inhabited by little robots that live continuously, interact with the world and each other, and learn — lifelong, from their own experience, driven only by curiosity and survival. A more "real" Game of Life.

There are no episodes, no resets, no tasks, and no fitness functions. One world runs for days; robots forage, dig, build, signal, hibernate, and die in it; each learning robot carries its own DreamerV3-style world model trained online from its own life. The point is watching what emerges.

The research

Despite the name this is a research platform, not a game — though like Conway's original it's a zero-player one: nothing to win, everything to watch. The overarching question: what behaviors, traits, and systems emerge in a world with no goals? No designer assigns objectives and no fitness function ranks outcomes — there are only bodies that need energy and can die, minds that are curious and get bored, and a shared world that other minds keep changing. Whatever shows up — foraging styles, individuality, signaling conventions, culture — has to come from that alone.

Concrete questions structure the long runs (in full: research questions). Four are about what an agent learns and does:

1. Lifelong, non-episodic world-model learning. The world-model literature (DreamerV3, Plan2Explore, TD-MPC2) resets its agents thousands of times into a stationary environment. Can a Dreamer-class agent learn through one unbroken life in a world that never holds still? Watch for plasticity loss and forgetting of places not seen for a sim-day.
2. Intrinsic motivation in a shared world. To a curiosity-driven agent, the most unpredictable object around is another curiosity-driven agent — uncertainty about a moving target never fully resolves. Mutual fascination? Avoidance once modeled? Chasing? An ablation flag masks other agents out of the curiosity signal to compare.
3. Emergent communication. The 2-channel broadcast costs a little energy and means nothing by design. Does it acquire meaning under survival pressure — bursts at food discoveries, distress near death, silence at night?
4. Cultural transmission. When newborns are warm-started from living agents' weights, do behaviors — foraging routes, hoarding sites — propagate across "generations"?

Four more are about what the system becomes over time — these the long runs surfaced on their own rather than being planned, and they answer the "no goals" question most directly:

5. What keeps a mind motivated for a whole lifetime? The project's central question, discovered in the runs: curiosity is self-extinguishing — master the world and there's nothing left to be surprised by — so how does motivation stay alive across one life, handing off between drives as each satisfies itself?
6. Where does individuality come from? Identical minds with identical drives, in a shared world, have already diverged into distinct and persistent personalities. Do they have to, and what carries the identity?
7. Does the world itself evolve under the population? A persistent world shaped by adaptive agents generates selection pressure back at them — an accidental plant-defense ecology already appeared under grazing pressure, with no ecology designed in.
8. Can natural selection arise without a fitness function? If reproduction becomes a bodily process funded by an agent's own energy surplus, does a population under no fitness function develop real selection and a cross-generational competence ratchet?

Every long run is attributable to at least one of these, and every round of runs gets a written-up finding in the research journal — negative and confounded results included, since the entries are the durable record after a save is pruned. The early rounds already produced surprises: an accidental plant-defense ecology under grazing pressure, curiosity collapsing once the world became predictable, and behavioral individuality arriving before survival competence.

Quick start

uv sync
uv run gol-run saves/alpha --new          # create a world and watch it in Rerun
uv run gol-run saves/alpha --resume       # continue where it left off
uv run gol-ctl pause                      # control a running world (speed/checkpoint too)
uv run gol-stats saves/alpha              # dig through metrics and events
uv run gol-stats saves/alpha --compare    # are the dreamers pulling ahead of chance?
uv run gol-stats saves/alpha --interests  # do agents differ, and stay themselves?
scripts/soak.sh saves/soak_001            # overnight run, restart-on-crash
scripts/provision_runpod.sh root@gpu-box saves/alpha   # ship a world to a cloud GPU

The shape of the thing

• World: finite Minecraft-like voxel world (terrain, water, ore, food bushes, day/night). Blocks are diggable and placeable. Physics is Minecraft-style AABB-vs-voxel — rich and modifiable, but cheap.
• Robots: wheeled bodies with color raycast vision (depth + shaded RGB — what a block is has to be read from how it looks) and a steerable gaze, plus a gripper, touch, an energy store, and a free 2-channel signal broadcast. Food restores energy; night stops regrowth; running out means hibernation, then death — which drops scrap back into the world.
• Brains: pluggable. Scripted baselines (random walker, forager) share the world with learning agents: per-robot DreamerV3-style agents (RSSM world model, imagination-trained actor-critic) driven only by intrinsic rewards — Plan2Explore curiosity plus bodily drives (homeostasis, hunger, boredom). No task reward exists anywhere in the codebase.
• Observability: Rerun — live 3D scene, per-agent charts (energy, curiosity, prediction error), scrubbable timelines, recordings.
• Compute tiers: 1–2 learning robots locally (Apple Silicon); populations of 8–16 on a single rented cloud GPU. Worlds checkpoint atomically and resume anywhere.
• Minds outlive bodies: with inherit_weights: lineage (what the standard run configs use), a learning brain's weights and memory carry over to its respawned body — death is costly, but the lineage keeps learning. none and random_living exist for the cultural-transmission experiments.
• Experiments: configs/run/exp_*.yaml are pre-registered protocols for the research questions (social curiosity with/without agent-masked curiosity, cultural transmission across inheritance modes). Long-run rounds get their own save-name-prefixed configs (beta_NN_*.yaml) that freeze at launch, so every save dir is a reproducible experiment.

Docs

• Architecture — full design: world, brains, runtime, milestones
• Research questions — what this platform is built to probe
• Research journal — findings from long runs, one entry per round
• Research proposals — designs argued out before they land
• Configs — how config layering and frozen round configs work