MAML for RL (Model-Agnostic Meta-Learning for Reinforcement Learning) applies the MAML meta-learning algorithm to enable RL agents to quickly adapt to new tasks with minimal environment interactions.

What Is MAML for RL?

• Goal: Learn initialization that adapts to new tasks in few gradient steps
• Method: Bi-level optimization over distribution of RL tasks
• Adaptation: Few episodes (10-100) in new environment
• Foundation: Finn et al. 2017 extended to policy gradient methods

Why MAML for RL Matters

Standard RL requires millions of samples per task. Meta-RL enables robots and agents to adapt to new situations within minutes, not days.

# MAML for RL Algorithm:
for meta_iteration in training:
    for task in sampled_tasks:
        # Inner loop: adapt to task
        policy_adapted = policy.clone()
        trajectories = collect_rollouts(policy_adapted, task)
        loss = compute_policy_gradient(trajectories)
        policy_adapted = policy_adapted - α * grad(loss)
        
    # Outer loop: meta-update
    meta_loss = sum(evaluate(policy_adapted, task) for task in tasks)
    policy = policy - β * grad(meta_loss, policy)

MAML vs. Other Meta-RL: