protomotions.agents.evaluators.mimic_evaluator module

class protomotions.agents.evaluators.mimic_evaluator.MimicEvaluator(agent, fabric, config)

Bases: BaseEvaluator

Evaluator for Mimic agent’s motion tracking performance.

__init__(agent, fabric, config)

Initialize the Mimic evaluator.

Parameters:

• agent (Any) – The Mimic agent to evaluate

• fabric (Any) – Lightning Fabric instance for distributed training

property motion_lib: MotionLib

Motion library (from agent).

property num_envs: int

Number of environments (from agent).

property motion_manager: MimicMotionManager

Motion manager (from env).

initialize_eval()

Initialize metrics dictionary with required keys.

Returns:

Dictionary of initialized MotionMetrics

Return type:

Dict

run_evaluation(metrics)

Run evaluation across multiple motions.

Parameters:

metrics (Dict) – Dictionary to collect evaluation metrics

evaluate_episode(metrics, active_env_ids, active_motion_ids)

Evaluate a single episode for a batch of motions.

Resets the environment with the specified motions and steps through the episode until completion or max steps, accumulating metrics.

Parameters:

• metrics (Dict) – Dictionary to collect evaluation metrics.

• active_env_ids (MockTensor) – Tensor of environment IDs to use for this batch.

• active_motion_ids (MockTensor) – Tensor of motion IDs to evaluate in these environments.

add_extra_obs_to_agent(obs)

update_metrics_from_env_extras(metrics, extras, active_env_ids, active_motion_ids)

Update metrics from env.extras.

Parameters:

• metrics (Dict) – Dictionary to update with metrics

• extras (Dict) – Dictionary of extra information from environment step

• motion_ids – Tensor of motion IDs being evaluated

process_eval_results(metrics)

Process results and check for early termination.

Parameters:

metrics (Dict) – Dictionary of collected metrics

Returns:

• Dict of processed metrics for logging

• Optional score value for determining best model

Return type:

Tuple containing

cleanup_after_evaluation()

Clean up after evaluation (reset env state, etc.)

simple_test_policy(collect_metrics=False)

Evaluates the policy in evaluation mode.

Parameters:

• collect_metrics (bool) – whether to collect metrics from the evaluation

• True (Will print the metrics to the console if collect_metrics is)