Reinforcement Learning Workflow

1. Train your 1^st RL Agent

Reference: srb agent train — Train Agent

Train online with srb agent train; offline algorithms use separate workflows.

Watch SBX PPO on landing; omit --headless:

srb agent train --algo sbx_ppo --env landing env.num_envs=512 --hide_ui

Check terminal progress; checkpoints save regularly. Usually stop with Ctrl+C.

2. Evaluate your Agent

Reference: srb agent eval — Evaluate Agent

srb agent eval --algo sbx_ppo --env landing env.num_envs=16

srb agent eval --algo sbx_ppo --env landing env.num_envs=16 --model logs/landing/sbx_ppo/${TIMESTAMP}/ckpt/${CHECKPOINT}

Note: SB3/SBX .zip checkpoints live in ckpt/.

3. Try a Different Algorithm

Type	Framework algorithms
Model-based	DreamerV3: `dreamer`; TD-MPC2: `tdmpc2`
On-policy	Stable-Baselines3 (SB3): `sb3_a2c`, `sb3_ppo`, `sb3_ppo_lstm`, `sb3_trpo`; SBX: `sbx_ppo`; RSL-RL: `rsl_rl_ppo`; skrl: `skrl_a2c`, `skrl_ppo`, `skrl_ppo_rnn`, `skrl_rpo`, `skrl_trpo`
Off-policy	SB3: `sb3_ddpg`, `sb3_td3`, `sb3_sac`, `sb3_crossq`, `sb3_tqc`; SBX: `sbx_ddpg`, `sbx_td3`, `sbx_sac`, `sbx_crossq`, `sbx_tqc`; skrl: `skrl_ddpg`, `skrl_td3`, `skrl_sac`
Evolutionary	SB3: `sb3_ars`

skrl: Hyperparameter profiles are untuned starting points.

srb agent train --headless --algo <ALGO> --env landing env.num_envs=1024

Hint: --headless and more parallel environments can speed training.

4. Monitor Training Progress

Compare runs with TensorBoard; local artifacts and TensorBoard are on by default. W&B is opt-in:

tensorboard --logdir ./logs --bind_all

DreamerV3: srb agent train ... 'agent.logger.outputs=[tensorboard,wandb]'
SB3 & SBX: srb agent train ... +agent.wandb=true
skrl: srb agent train ... agent.agent.experiment.wandb=true

Note: W&B logging requires an account and API key.

Each run records its provenance and metrics; see Run records.

5. Configure Hyperparameters

Reference: Agent Configuration

srb agent train --algo dreamer      agent.run.train_ratio=128  ...
srb agent train --algo sb3_ppo      agent.gamma=0.99           ...
srb agent train --algo sbx_sac      agent.learning_rate=0.0002 ...
srb agent train --algo skrl_ppo     agent.models.separate=True ...

Next: Hyperparameter Tuning

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