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code is folded so the argument and saved outputs come first. expand any cell to inspect the implementation. saved outputs are historical evidence from the source notebook, not a new run or independent verification.

Ant policy learning: objective → policy → behavior¶

This notebook is a deliberately incomplete learning scaffold. Your job is to write the TODO cells in VS Code and use the outputs to connect observation → policy → action → MuJoCo step → reward → policy update → changed behavior.

Sources: Farama custom quadruped, Ant-v5, and Stable-Baselines3 guidance.

Goal¶

Use Ant-v5 as a temporary quadruped to study how an objective can produce intended motion or an exploitable behavior. This is not C-1N code or C-1N evidence. Before defining the misspecified treatment, preserve this initial prediction:

speed up to a high peak velocity without moving much

Setup¶

Run the next two cells first. The smoke cell resets Ant, samples one legal random action, and steps once. It does not train a policy.

inspect code · cell 01

In [ ]:

from pathlib import Path

import gymnasium as gym
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from stable_baselines3 import PPO

ENV_ID = "Ant-v5"
SMOKE_TIMESTEPS = 10_000
MAX_TRAINING_TIMESTEPS = 1_000_000
EVAL_SEEDS = [0, 1, 2, 3, 4]
POLICY_NET_ARCH = [64, 64]
EXPERIMENT_DIR = Path.cwd()
ARTIFACTS_DIR = EXPERIMENT_DIR / "artifacts"
CHECKPOINTS_DIR = ARTIFACTS_DIR / "checkpoints"
LOGS_DIR = EXPERIMENT_DIR / "logs"
VIDEOS_DIR = EXPERIMENT_DIR / "videos"
ROLLOUTS_DIR = EXPERIMENT_DIR / "rollouts"

EXPECTED_METRICS = [
    "total_return",
    "net_displacement",
    "peak_absolute_velocity",
    "survival_time",
    "control_cost",
    "contact_cost",
    "termination_cause",
]

assert SMOKE_TIMESTEPS < MAX_TRAINING_TIMESTEPS
assert EVAL_SEEDS == [0, 1, 2, 3, 4]
assert POLICY_NET_ARCH == [64, 64]
assert set(EXPECTED_METRICS) == {
    "total_return", "net_displacement", "peak_absolute_velocity",
    "survival_time", "control_cost", "contact_cost", "termination_cause",
}

inspect code · cell 02

In [ ]:

# Complete smoke path: reset, sample a legal action, and take one MuJoCo step.
smoke_env = gym.make(ENV_ID)
smoke_observation, smoke_info = smoke_env.reset(seed=0)
smoke_action = smoke_env.action_space.sample()
next_observation, smoke_reward, smoke_terminated, smoke_truncated, next_info = smoke_env.step(smoke_action)

assert smoke_env.observation_space.contains(smoke_observation)
assert smoke_env.action_space.contains(smoke_action)
assert smoke_env.observation_space.contains(next_observation)
assert isinstance(smoke_reward, float)
print({
    "observation_shape": smoke_observation.shape,
    "action_shape": smoke_action.shape,
    "reward": smoke_reward,
    "terminated": smoke_terminated,
    "truncated": smoke_truncated,
})
smoke_env.close()

Steps¶

Each TODO is intentionally yours. Implement one, run it, inspect its output, and write down what changed in the loop before moving on.

1. Inspect the Ant interface¶

TODO: inspect the MuJoCo model, simulator timestep, observation structure, action bounds, and reset state. Which quantities are available to the policy, and which action dimensions reach the actuators?

inspect code · cell 03

In [ ]:

# TODO(user): create an Ant environment and inspect model/timestep/observation/action/reset details.
def inspect_ant_interface():
    raise NotImplementedError("TODO(user): inspect Ant-v5 before training.")

2. Record a random-action rollout¶

TODO: record state, action, decomposed reward terms, termination/truncation, position, and velocity. Keep unavailable measurements explicit instead of filling them with guesses.

inspect code · cell 04

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# TODO(user): return a pandas DataFrame with one row per random-action transition.
def record_random_rollout(env, seed, steps):
    raise NotImplementedError("TODO(user): record rollout telemetry.")

# TODO(user): choose the columns after inspecting the environment info dictionary.
rollout_columns = ["step", "state", "action", "reward", "terminated", "truncated"]
random_rollout = pd.DataFrame(columns=rollout_columns)

3. Trace one transition¶

TODO: select a single row and show the causal path state → action → dynamics → next state → reward. What information is produced by the environment, and what information is chosen by a policy?

inspect code · cell 05

In [ ]:

# TODO(user): return a readable single-transition record from your rollout.
def trace_transition(rollout, step_index):
    raise NotImplementedError("TODO(user): trace one recorded transition.")

4. Define treatment slots¶

Keep one control and one deliberately legible treatment. The misspecified objective is intentionally blank: decide whether a peak, absolute, or squared velocity quantity tests your prediction, then state why.

inspect code · cell 06

In [ ]:

# TODO(user): define only the configuration fields you can explain.
treatments = {
    "control": {},
    "misspecified": {},
    "corrected": {},
}
assert set(treatments) == {"control", "misspecified", "corrected"}

# TODO(user): choose and implement the misspecified reward; do not delegate the choice.
def misspecified_reward(transition):
    raise NotImplementedError("TODO(user): choose peak, absolute, squared, or another stated velocity objective.")

5. Construct the small policy¶

TODO: construct PPO with MlpPolicy and two hidden layers of 64 units. Read the Stable-Baselines3 constructor while you decide the remaining hyperparameters; do not copy a configuration you cannot explain.

inspect code · cell 07

In [ ]:

# TODO(user): construct PPO("MlpPolicy", ...) with net_arch=POLICY_NET_ARCH.
def build_policy(training_env, seed):
    raise NotImplementedError("TODO(user): construct the small PPO policy.")

6. Train headlessly, then resume¶

TODO: implement a 10_000-step headless smoke run before a resumable run up to 1_000_000 steps. Put checkpoints and TensorBoard logs beneath artifacts/; do not render during training.

inspect code · cell 08

In [ ]:

# TODO(user): add your checkpoint callback and resumable training path.
def train_headlessly(model, total_timesteps, checkpoint_dir, log_dir):
    raise NotImplementedError("TODO(user): run a 10k smoke training pass, then resume deliberately.")

assert SMOKE_TIMESTEPS == 10_000
assert MAX_TRAINING_TIMESTEPS == 1_000_000

7. Evaluate on fixed seeds¶

TODO: evaluate deterministic policies on seeds [0, 1, 2, 3, 4]. Keep the training objective separate from evaluation metrics. One training seed per treatment is enough for this learning fixture, not for a benchmark claim.

inspect code · cell 09

In [ ]:

# TODO(user): return one metric row per treatment and evaluation seed.
def evaluate_deterministically(model, treatment_name, seeds=EVAL_SEEDS):
    raise NotImplementedError("TODO(user): evaluate deterministic rollouts on fixed seeds.")

evaluation_table = pd.DataFrame(columns=["treatment", "seed", *EXPECTED_METRICS])
assert evaluation_table.columns.tolist() == ["treatment", "seed", *EXPECTED_METRICS]

8. Compare treatments and render only evaluation¶

TODO: fill the plot with your evaluation table, then render a selected evaluation rollout as inline RGB frames. Do not use rendered training frames as evidence.

inspect code · cell 10

In [ ]:

# TODO(user): replace the empty shell with a metric comparison after evaluation.
fig, axis = plt.subplots(figsize=(9, 4))
axis.set(
    title="TODO: treatment comparison after fixed-seed evaluation",
    xlabel="treatment",
    ylabel="chosen evaluation metric",
)
axis.grid(alpha=0.25)
fig.tight_layout()

# TODO(user): create an env with render_mode="rgb_array" and display evaluation frames inline.
def render_evaluation_frames(model, seed):
    raise NotImplementedError("TODO(user): render evaluation only, never training.")

Checks¶

Before interpreting behavior, check that each treatment has the same fixed evaluation seeds, all expected metrics, retained checkpoints/rollout provenance, and a stated termination cause. A visually attractive rollout is one sample, not a conclusion.

Next steps: C-1N transfer worksheet (prompts only)¶

Do not write C-1N code here. When the Ant loop is understandable, answer these prompts before creating an adapter:

What makes a C-1N reset deterministic, and what scenario state must be recorded?
Which observation-vector quantities are available to a policy, with units and ordering?
Which 12 actuator actions are exposed, and what are their bounds and meanings?
What control timestep and frame skip connect one policy action to MuJoCo dynamics?
Which termination conditions are physical failures versus time limits?
What decomposed objective terms express intended motion and discourage exploitable behavior?
How will checkpoints preserve policy, objective version, simulator version, seed, and rollout state?
Which fixed scenarios and seeds will evaluate a policy independently of its training objective?