srb agent eval — Evaluate Agent
The srb agent eval command runs a trained policy in evaluation mode against a registered environment. The policy can be from any RL or IL framework integrated with SRB (sb3_*, sbx_*, skrl_*, rsl_rl_ppo, dreamer, tdmpc2, robomimic_*).
Usage
srb agent eval --env ENV_ID (--algo ALGO | --model MODEL) [options]
Options | Shared Agent Options
| Option | Description | Default |
|---|---|---|
--algo ALGO | Algorithm of the policy to evaluate. Loads the latest checkpoint from the standard log directory unless --model is also given. | unset |
--model PATH | Path to a specific checkpoint or SRB model artifact. The algorithm is inferred from the path when --algo is omitted. | unset |
--obs {state,visual} | Observation modality: selects the agent-config variant and the log tree searched for the latest checkpoint (<algo> vs <algo>-visual). | state |
Note: At least one of
--algoor--modelmust be provided; otherwise the command exits with an error.
Supported Algorithm Families
The parser accepts registered policy algorithm names such as dreamer, tdmpc2, sbx_ppo, sb3_ppo, rsl_rl_ppo, skrl_ppo, robomimic_bc, and robomimic_cql.
Examples
# Latest checkpoint of an SBX PPO policy on landing
srb agent eval --env landing --algo sbx_ppo env.num_envs=16
# Explicit checkpoint, algorithm inferred from the path
srb agent eval --env landing \
--model space_robotics_bench/logs/landing/sbx_ppo/<run>/ckpt/<ckpt>
# Portable SRB model artifact, including bundled restore config when available
srb agent eval --env excavation \
--model /models/srb_excavation_kinova_gen3_smooth_osc_rsl_rl_ppo
When --model points to an SRB model artifact, evaluation uses the shared policy loader used by collect and real-world validation. That preserves bundled framework restore context such as Dreamer, TD-MPC2, or RSL-RL config.yaml files instead of depending on the original training log directory.
For a complete walkthrough see the Reinforcement Learning Workflow and the Imitation Learning Workflow.