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Task 6: DreamerV3 CPG on Acrobot (second published-model adapter) #49

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Task 6: DreamerV3 CPG on Acrobot (second published-model adapter) #49

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e5ae8ed
chore(roadmap): mark Task 6 (DreamerV3 Acrobot CPG) in_flight
Denis-hamon Jun 9, 2026
32c01b2
fix(dreamerv3): force osmesa env for training subprocess
Denis-hamon Jun 9, 2026
32e2775
results(dreamerv3): Acrobot CPG seed 0 + refreshed MODEL_TABLE
Denis-hamon Jun 10, 2026
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2 changes: 1 addition & 1 deletion experiments/GPU_ROADMAP.md
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Expand Up @@ -145,7 +145,7 @@ pooled-30 cells are MODEL BOTTLENECK; see PR.
**Branch**: `phase-5y-dreamerv3-acrobot`
**Effort**: ~10-20 h GPU (500k env steps, dmc_proprio config, single env)
**Priority**: HIGH (multi-model generality claim; everything but GPU time is already shipped)
**Status**: queued
**Status**: in_flight

**Steps**:

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8 changes: 7 additions & 1 deletion experiments/dmc_acrobot/dreamerv3_cpg.py
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Expand Up @@ -212,7 +212,13 @@ def _train_dreamerv3(
cmd += ["--prefill", "500", "--eval_every", "1000", "--log_every", "500"]
workdir.mkdir(parents=True, exist_ok=True)
print(f"[train] {' '.join(cmd)}")
subprocess.run(cmd, cwd=str(_DREAMER_PKG), check=True)
# Upstream's dreamer.py hard-sets MUJOCO_GL=osmesa, but this process
# defaults MUJOCO_GL=egl (line ~76) for the CPG arms' dm_control import.
# That egl leaks into the child env and dm_control then refuses to start
# ("PYOPENGL_PLATFORM is set to 'egl', should be unset or 'osmesa'").
# Force the osmesa render path the subprocess actually uses.
train_env = {**os.environ, "MUJOCO_GL": "osmesa", "PYOPENGL_PLATFORM": "osmesa"}
subprocess.run(cmd, cwd=str(_DREAMER_PKG), check=True, env=train_env)


def _port_agent_checkpoint(
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2 changes: 2 additions & 0 deletions results/MODEL_TABLE.md
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Expand Up @@ -12,6 +12,7 @@ planner outperforms the same planner on the learned dynamics.

| Environment | Model | Planner | Init | n/arm | Oracle | Learned | CPG | 95% AC CI | Verdict |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| dmc_acrobot_swingup | dreamerv3 | random-shooting | varied | 10 | 0.100 | 0.100 | +0.000 | [-0.298, +0.298] | INCONCLUSIVE |
| dmc_acrobot_swingup | learned MLP | random-shooting | varied | 10 | 0.100 | 0.100 | +0.000 | [-0.298, +0.298] | INCONCLUSIVE |
| dmc_acrobot_swingup | mlp_on_tdmpc2_data | cem | varied | 150 | 0.033 | 0.020 | +0.013 | [-0.027, +0.053] | PLANNER BOTTLENECK |
| dmc_acrobot_swingup | mlp_on_tdmpc2_data | random-shooting | varied | 10 | 0.100 | 0.100 | +0.000 | [-0.298, +0.298] | INCONCLUSIVE |
Expand All @@ -32,6 +33,7 @@ planner outperforms the same planner on the learned dynamics.

## Row sources

- `dmc_acrobot_swingup` / `dreamerv3` / `random-shooting` / varied: `results/dmc_acrobot/dreamerv3_cpg.json`
- `dmc_acrobot_swingup` / `learned MLP` / `random-shooting` / varied: `results/dmc_acrobot/cpg.json`
- `dmc_acrobot_swingup` / `mlp_on_tdmpc2_data` / `cem` / varied: `results/dmc_acrobot/cem_cpg_sweep.json`
- `dmc_acrobot_swingup` / `mlp_on_tdmpc2_data` / `random-shooting` / varied: `results/dmc_acrobot/coverage_mlp_on_tdmpc2_cpg.json`
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