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name: training-workflows description: "Use AgileRL classical single-agent training workflows for Gymnasium PPO, DQN, RainbowDQN, DDPG, TD3, replay/rollout buffers, and distributed training setup." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AgileRL Training Workflows

Use this sub-skill for AgileRL classical single-agent RL training with Gymnasium environments: PPO on-policy, DQN/RainbowDQN/DDPG/TD3 off-policy, recurrent variants, population setup, replay or rollout buffers, and safe pre-flight checks before long runs.

Read First

• references/workflows.md for on-policy/off-policy training recipes and population flow.
• references/api-reference.md for important training helper and buffer APIs.
• references/configuration.md for INIT_HP, net_config, YAML, logging, and environment patterns.
• references/distributed-training.md for Accelerate/distributed constraints.
• references/troubleshooting.md for shape, dependency, vector-env, W&B, and runtime failures.
• scripts/agilerl_classic_smoke.py --help for a no-training import/config probe.

Route Within AgileRL

Need	Route
Evolution/tournament/mutation configuration	../hpo-and-mutation/SKILL.md
Custom net_config, EvolvableNetwork, CNN/LSTM/MultiInput setup	../evolvable-modules/SKILL.md
Multi-agent PettingZoo training	../multi-agent-and-wrappers/SKILL.md
Offline datasets or contextual bandits	../offline-bandits-data/SKILL.md
LLM fine-tuning	../llm-fine-tuning/SKILL.md

Standard Classical Training Flow

1. Install AgileRL and verify imports; use the root scripts/check_agilerl_install.py if unsure.
2. Create or wrap a Gymnasium environment; prefer make_vect_envs(env_name, num_envs=...) for vectorized classical training.
3. Define INIT_HP with algorithm-specific keys and POP_SIZE.
4. Define net_config with encoder_config and head_config unless the default network is sufficient.
5. Create a population with create_population(algo=..., observation_space=..., action_space=..., net_config=..., INIT_HP=..., population_size=..., num_envs=..., device=...).
6. For off-policy algorithms, create ReplayBuffer; for on-policy PPO, use rollout collection/training helpers rather than replay memory.
7. Configure TournamentSelection and Mutations in the HPO sub-skill.
8. Call train_on_policy(...) or train_off_policy(...), or implement a custom loop following references/workflows.md.

Algorithm Selection

• Use PPO for on-policy, stable policy-gradient workflows and recurrent PPO variants.
• Use DQN or RainbowDQN for discrete-action off-policy value learning.
• Use DDPG or TD3 for continuous-action off-policy actor-critic workflows.
• Use train_on_policy for PPO and train_off_policy for DQN/RainbowDQN/DDPG/TD3 families.

Safe Validation

Run a smoke check before training:

python scripts/agilerl_classic_smoke.py --algorithm DQN --env CartPole-v1
python scripts/agilerl_classic_smoke.py --algorithm PPO --env CartPole-v1

The helper checks imports, Gymnasium spaces, basic config shape, and algorithm family routing. It does not train or download anything.