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Advanced Mava usage 👽

Data recording from a PPO system 🔴

We include here an example of an advanced use case with Mava: recording experience data from a PPO system, which can then be used for offline MARL—e.g. using the OG-MARL framework. This functionality is demonstrated in ff_ippo_store_experience.py, and uses Flashbax's Vault feature. Vault enables efficient storage of experience data recorded in JAX-based systems, and integrates tightly with Mava and the rest of InstaDeep's MARL ecosystem.

Firstly, a vault must be created using the structure of an experience buffer. Here, we create a dummy structure of the data we want to store:

# Transition structure
dummy_flashbax_transition = {
    "done": jnp.zeros((config.system.num_agents,), dtype=bool),
    "action": jnp.zeros((config.system.num_agents,), dtype=jnp.int32),
    "reward": jnp.zeros((config.system.num_agents,), dtype=jnp.float32),
    "observation": jnp.zeros(
        (
            config.system.num_agents,
            env.observation_spec().agents_view.shape[1],
        ),
        dtype=jnp.float32,
    ),
    "legal_action_mask": jnp.zeros(
        (
            config.system.num_agents,
            config.system.num_actions,
        ),
        dtype=bool,
    ),
}

# Flashbax buffer
buffer = fbx.make_flat_buffer(
    max_length=int(5e5),
    min_length=int(1),
    sample_batch_size=1,
    add_sequences=True,
    add_batch_size=(
        n_devices
        * config["system"]["num_updates_per_eval"]
        * config["system"]["update_batch_size"]
        * config["arch"]["num_envs"]
    ),
)

# Buffer state
buffer_state = buffer.init(
    dummy_flashbax_transition,
)

We can now create a Vault for our data:

v = Vault(
    vault_name="our_system_name",
    experience_structure=buffer_state.experience,
    vault_uid="unique_vault_id",
)

We modify our learn function to additionally record our agents' trajectories, such that we can access experience data:

learner_output, experience_to_store = learn(learner_state)

Because of the Anakin architecture set-up, our trajectories are stored in the incorrect dimensions for our use case. Hence, we transform the data, and then store it in a flashbax buffer:

# Shape legend:
# D: Number of devices
# NU: Number of updates per evaluation
# UB: Update batch size
# T: Time steps per rollout
# NE: Number of environments

@jax.jit
def _reshape_experience(experience: Dict[str, chex.Array]) -> Dict[str, chex.Array]:
    """Reshape experience to match buffer."""

    # Swap the T and NE axes (D, NU, UB, T, NE, ...) -> (D, NU, UB, NE, T, ...)
    experience: Dict[str, chex.Array] = jax.tree.map(lambda x: x.swapaxes(3, 4), experience)
    # Merge 4 leading dimensions into 1. (D, NU, UB, NE, T ...) -> (D * NU * UB * NE, T, ...)
    experience: Dict[str, chex.Array] = jax.tree.map(
        lambda x: x.reshape(-1, *x.shape[4:]), experience
    )
    return experience

flashbax_transition = _reshape_experience(
    {
        # (D, NU, UB, T, NE, ...)
        "done": experience_to_store.done,
        "action": experience_to_store.action,
        "reward": experience_to_store.reward,
        "observation": experience_to_store.obs.agents_view,
        "legal_action_mask": experience_to_store.obs.action_mask,
    }
)
# Add to fbx buffer
buffer_state = buffer.add(buffer_state, flashbax_transition)

Then, periodically, we can write this buffer state into the vault, which is stored on disk:

v.write(buffer_state)

If we now want to use the recorded data, we can easily restore the vault in another context:

v = Vault(
    vault_name="our_system_name",
    vault_uid="unique_vault_id",
)
buffer_state = v.read()

For a demonstration of offline MARL training, see some examples here.

⚠️ Note: this functionality is highly experimental! The current API likely to change.