Agent Configuration
Configure agents with Hydra.
srb agent train --algo sb3_ppo --env peg_in_hole \
agent.learning_rate=0.0003 \
agent.batch_size=128
Default Hyperparameters
Use hyperparams/ for defaults. Add optional task overrides in hyperparams/task/.
Modifying Configurations
Edit framework defaults or pass Hydra overrides.
Configuration Files
Defaults are grouped by framework (YAML except Robomimic’s JSON):
hyperparams/
├── sb3/ # Stable Baselines 3 (ppo, sac, td3, ...)
├── sbx/ # SB3 eXtensions
├── skrl/ # SKRL (ppo, a2c, ppo_rnn, ...)
├── rsl_rl/ # RSL RL
├── robomimic/ # Robomimic
├── dreamerv3.yaml # DreamerV3 (single-algorithm framework)
└── tdmpc2.yaml # TD-MPC2 (single-algorithm framework)
Use framework-prefixed algorithm names (sb3_ppo, sbx_sac, skrl_ppo, rsl_rl_ppo, dreamer, tdmpc2) and pass Hydra overrides on the command line.
Note: For algorithms with a registered agent dictionary,
--cfgreplaces the wholeagentmapping. Include every required setting; omitted registered values are dropped.
SKRL SAC
hyperparams/skrl/sac.yaml pins SRB’s bounded/squashed Gaussian policy, two
critics and target critics, plus explicit terminated/truncated replay flags.
skrl_sac supports train, eval, and policy collection.
Tuned configurations
srb agent tune writes the requested modality’s config, resolving task-specific → global layers:
hyperparams/<framework>/<algo>.yaml— global per-algorithm set (state observations);<algo>_visual.yamlfor visual observations.hyperparams/task/<task>/<framework>/<algo>.yaml— optional per-task override that takes precedence for that task only.
The # Tuned by srb agent tune … provenance header records study, trial, objective, seeds, date, git SHA, and Optuna version. See Hyperparameter Tuning.
Visual profiles (hyperparams/visual_profiles.yaml) do not ship. Optuna tuning spaces are included under srb/tuning/spaces. Without them, train and eval reject --obs visual; use --obs state or supply a valid catalog. tune supports only --obs state.