meta-rl

**Meta-RL** (Meta-Reinforcement Learning) is the **application of meta-learning to reinforcement learning** — training an agent on a distribution of tasks so that it can rapidly adapt to new, unseen tasks with very little experience, effectively "learning to learn" optimal policies. **Meta-RL Approaches** - **Recurrent**: Train an RNN policy across task episodes — the hidden state encodes task information (RL², SNAIL). - **Gradient-Based**: Use MAML to learn an initialization that adapts quickly to new tasks with few gradient steps. - **Context-Based**: Learn a task encoder that infers the task from experience and conditions the policy. - **Hypernetwork**: Generate task-specific policy parameters from a meta-learner. **Why It Matters** - **Fast Adaptation**: Meta-RL agents adapt to new tasks in a few episodes, not thousands. - **Transfer**: Captures common structure across tasks — transfers to novel but related tasks. - **Semiconductor**: A meta-RL agent could quickly adapt to new process conditions or product recipes. **Meta-RL** is **learning to learn policies** — training an agent that rapidly masters new tasks by leveraging meta-knowledge from many previous tasks.