Reward Conditioned Policy Learning From Task Outcomes

via “reward function design and shaping for complex multi-objective tasks”

* ⭐ 02/2022: [Magnetic control of tokamak plasmas through deep reinforcement learning](https://www.nature.com/articles/s41586-021-04301-9%E2%80%A6)

Unique: Combines potential-based reward shaping with multi-objective weighting to balance lap time, safety, and race position, using domain knowledge about racing physics to structure rewards that guide learning without over-constraining agent behavior or creating conflicting gradient signals

vs others: Achieves better policy robustness than single-objective rewards (lap time only) by explicitly balancing safety and race performance, and better sample efficiency than inverse RL approaches by leveraging domain knowledge to structure rewards directly

via “reward shaping and curriculum learning for complex locomotion tasks”

* ⭐ 10/2022: [Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)](https://www.nature.com/articles/s41586-022%20-05172-4)

Unique: Combines multi-component reward shaping with progressive curriculum learning, where task difficulty increases automatically as policy performance improves, enabling stable training toward complex locomotion objectives

vs others: Guides RL training toward natural, energy-efficient gaits by decomposing objectives into weighted reward components and progressively increasing difficulty, compared to sparse reward or single-objective approaches

via “reward-conditioned policy learning from task outcomes”

### Other Papers <a name="2023op"></a>

Unique: Directly optimizes language model policies for task outcomes without requiring intermediate action-level labels or human preferences, using trajectory-level rewards as the sole learning signal — this is distinct from RLHF which requires pairwise human comparisons

vs others: Simpler than RLHF because it avoids human annotation overhead, and more direct than supervised fine-tuning because it optimizes for actual task success rather than action imitation