multi-goal rl

**Multi-Goal RL** is a **reinforcement learning paradigm where the agent must learn to achieve multiple different goals** — training a single policy $pi(a|s,g)$ that can accomplish any goal from a goal space, rather than training separate policies for each goal. **Multi-Goal Approaches** - **Goal-Conditioned Policy**: Policy takes goal as input — $pi(a|s,g)$ outputs actions conditioned on the current goal. - **UVFA**: Universal value function $Q(s,a,g)$ estimates value for any state-action-goal triple. - **HER**: Hindsight Experience Replay — relabel failed trajectories with achieved goals for dense learning signal. - **Curriculum**: Automatically generate goals of increasing difficulty — adaptive goal curriculum. **Why It Matters** - **Generalization**: One agent handles a distribution of tasks — far more practical than single-task agents. - **Sample Efficiency**: Sharing experience across goals massively improves sample efficiency. - **Robotics**: A robot that can reach any position, grasp any object — multi-goal is the natural formulation. **Multi-Goal RL** is **one agent, many objectives** — training a versatile agent that accomplishes any goal from a continuous goal space.