Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment

Summary

This paper aims to alleviate the reward-misalignment issue by introducing task-related objective. The authors theoretically derive sufficient conditions to mitigate the task-reward misalignment issue and design algorithms accordingly. Theoretical analysis of the algorithm is provided to guarantee the reward learning improvement. Two discrete grid-world experiments are provided to empirically validate the proposed algorithms.

Strengths

1. The idea of introducing task objective to help alleviate the reward ambiguity issue is novel and interesting. 2. This paper has a complete and solid theoretical structure, which is also the biggest strength. 3. The empirical results show superior performance over GAIL and VAIL.

Weaknesses

The main weakness lies in the empirical evaluation: 1. The environment setups are too simple. The paper only studies two discrete settings where continuous environments are missing, e.g., MuJoCo. 2. The experiment only has two comparison baselines, which is not convincing enough to show the superiority of the proposed algorithm. The paper mentions the IRL based IL and aims to alleviate the reward ambiguity issue, why not directly use some IRL algorithms as baselines to compare, e.g., MaxEnt IRL, ML-IRL, f-IRL, etc. I know that this paper is a theory paper where the theoretical analysis is the major contribution and should be valued the most. However, the empirical evaluation should at least meet the minimum standard of the current majority, and two discrete gridworlds with fewer than 100 states apparently do not. I highly suggest the authors to add some continuous environments and baselines.

Questions

Please see weakness.

Rating

5

Confidence

3

Soundness

3

Presentation

2

Contribution

3

Limitations

The author discusses the limitation in the paper, which is reasonable.

Summary

This paper introduces a novel approach to inverse RL (IRL) by identify weaknesses in current IRL algorithms and proposing the respective solution: optimizing for *task-related* reward functions. The authors provide a clean theoretical framework for task-related rewards, but lack applicability in practice from my perspective (see below).

Strengths

- inverse RL with task-aligned reward functions is an interesting research direction. - the paper presents a good theoretical basis for future work. - the theoretical work looks plausible to me (only reading the main paper).

Weaknesses

- The storyline of the paper is not clear. I do not see the emphasis on task-relatedness (see comments below). - The paper is cluttered in theory, making it hard to follow. Instead, more focus should be on the story, the motivation, and the experiments. - The algorithm looks quite complex; not sure how scaleable it is. (suggestions below) - The experimental campaign is concentrated on simple tasks; more complex tasks could prove scalability.

Questions

- How is $\Pi_{acc}$ actually spanned? It is not really talked about it, which made the following sentence a bit confusing “It is crucial to recognize that rE might not meet the task-aligned reward function criteria specified in Definition 2, even though its optimal policy πE is acceptable. ” - Looking at Def 2, the “task relatedness” is only induced by the definition of $\Pi_{acc}$, is this correct?

Rating

5

Confidence

4

Soundness

2

Presentation

2

Contribution

2

Limitations

My biggest problem is that I dont see why this paper (in practice) proposes a technique for task-aligned reward functions. $k$ and $\Pi_{acc}$ define task-relatedness in this framework to my understanding. And as the authors say: “As the task alignment of each reward function typically remains unknown in IRL settings, this paper proposes treating k as an adjustable parameter – starting with a small k and adjusting based on empirical learning outcome, allowing for iterative refinement for alignment with task requirements.” So what defines task-relatedness is actually set as a hyperparameter in this framework. For me, the method sounds more like a regularization technique for finding less overfitting reward functions. It looks like the auhors also say so: “Given the uncertainty in identifying which reward function is aligned, our solution is to train a policy to achieve high utilities under all reward functions in RE,δ to satisfy the conditions in Definition 3.” --> all reward functions does not mean task-related reward functions. Hence, my claim of being a regularization technique rather than an active search for task-related search. For me, this is breaking the storyline of the paper. Further points: - In section 4.1, consider clarifying the difference between r* and r_E. It took me a bit do understand it. - Algorithm 1, line 4: I guess it should be $\pi_P$ instead of $\pi_A$. - an ablation on $\delta$ would be interesting - add more complex environments. One good fit could be the new locomotion benchmark LocoMuJoCo. It provides motion capture datasets mapped towards different robots. So, there is a dynamics mismatch between the expert dataset and the environment. Also, all baselines (Gail, Vail, IQ-Learn) are already available. - Since you have been using IQ-Learn [1] as a baseline, I would also add it together with similar methods using implicit rewards [2, 3] to the related work section with some discussion. Otherwise, it looks out of place in the experiment section. - General remark: shorten the theory and make a more comprehensive experimental campaign. [1] Garg et al.; IQ-Learn: Inverse soft-Q Learning for Imitation \ [2] Al-Hafez et al.; LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning \ [3] Sikchi et al.; Dual RL: Unification and new methods for reinforcement and imitation learning

Summary

This paper addresses the reward misalignment in inverse reinforcement learning and introduces a novel approach to tackle this problem: PAGAR. It introduces a protagonist and an antagonist policy and treats the expert demonstrations as weak supervision to derive a set of reward functions rather than a single reward function. The reward functions and policies are used in a min-max scheme to minimize the regret induced by the two policies. The antagonist policy should maximize the regret whereas the protagonist policy should minimize it. PAGAR is used on adversarial imitation learning settings (more specifically on top of GAIL and VAIL) in combination with PPO on two discrete domain tasks.

Strengths

* The problem of solving the underlying tasks instead of adapting to the expert data is very relevant. * Even though the idea takes inspiration from UED, the proposed approach is novel and significant for the IRL community. * The authors provide theoretical results.

Weaknesses

* Even though the relationship and inspiration to UED is mentioned once, the basis of UED should be explained more. * The experimental evaluation is limited. E.g. the experiments only contain environments with discrete action spaces. Since GAIL and VAIL as well as PPO work in continuous action spaces, it should not be a problem to include additional experiments on continuous control tasks. * Due to the introduced notation and presented theoretical results, the paper is difficult to follow. Even though the appendix already includes most of the theoretical derivations and proofs, some parts of the main paper could still be included there to give more space for explanation, intuition and experiments. E.g. section 5.1. could be moved to the appendix or integrated in a reduced version in the main text. * Minor: - Missing } in line 270. - Line 4 in algorithm 1: It should be "update $\pi_{P}$ to maximize..." instead of $\pi_{A}$ ?

Questions

See weaknesses.

Rating

7

Confidence

2

Soundness

3

Presentation

3

Contribution

3

Limitations

Even though the authors mention a small amount of limitations in the checklist, I think that setting the parameter $k$ is not the only limitation of the method (which is the only limitation addressed in the main text). An individual section about the limitations of the approach is missing.

Summary

This work investigates the inverse reinforcement learning problem. Previous methods suffer from the weakness: fail to capture the true task objectives from demonstrations. This study proposes deriving a set of candidate reward functions that align with the task rather than merely the data. Using an adversarial learning approach, the policy is trained with this set of reward functions. Then the proposed framework collectively validates the policy’s ability for task accomplishment. The proposed method is theoretically derived with detailed derivation and experimentally implemented on MiniGrid environments with impressive empirical results.

Strengths

The paper is well-structured, clearly explained, and nicely presented. The approach has a solid theoretical foundation. The method shows significant advantages over baseline approaches on MiniGrid DoorKey, especially with a single demonstration.

Weaknesses

The experiments on MiniGrid are not very extensive. Beyond DoorKey, MiniGrid includes more challenging environments such as ObstructedMaze and UnLock. Demonstrating advantages in these environments would be more convincing. The proposed approach requires a set of candidate reward functions. While it achieves a higher success rate than baselines, it is unclear if this comes at a higher time cost. Comparing the time efficiency with prior work would be beneficial.

Questions

In Appendix Figure 5, the proposed approach does not significantly better than the baselines in locomotion tasks (Walker, HalfCheetah, Hopper, Swimmer, etc.). Would the method show more advantages if only 1 demonstration is used instead of 10 or 100? The MiniGrid DoorKey 6x6 experiment just requires a demonstration with a short horizon. In larger mazes or more challenging environments with complicated demonstrations, would the proposed approach still perform impressively with only 1 demo?

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

There is no analysis explaining why the proposed method performs better on MiniGrid (Figure 2) than on locomotion tasks (Appendix Figure 5). Studying the scenarios where the method does not work well and providing explanations would help researchers determine the appropriateness of using this approach.

Suppplementary Experimental Results: Comparing PAGAR with f-IRL in Continuous Control Tasks

We thank the reviewers again for their valuable comments. We hope that our previous responses have addressed most of your concerns. To further support our rebuttal, we would like to share some additional experimental results. We have conducted experiments combining PAGAR with **f-IRL**[1], and the results are summarized in the following table. Our PAGAR-based approach substantially outperforms the baseline with fewer exploration iterations in the environment across multiple standard IRL/IL benchmarks in **continuous control**. Ideally, these should be presented as **plots** to better illustrate the performance trends. In the revised version of the paper, we will ensure that these results are presented appropriately. | Hopper | f-IRL (FKL) | (Ours) PAGAR-f-IRL (FKL) | |-------- |------- | ------------| |Env Step 0 ~ 2.5e5 | $937.55\pm251.59$ | $999.25\pm236.45$ | |Env Step 2.5e5 ~ 5e5 | $1168.47\pm191.36$ | $2051.02\pm424.40$ | |Env Step 5e5 ~ 7.5e5 | $1693.07\pm337.33$ | $2975.78\pm108.25$ | |Env Step 7.5e5 ~ 1e6 | $2637.24\pm304.30$ | $3159.55\pm84.38$ | | Ant | f-IRL (FKL) | (Ours) PAGAR-f-IRL (FKL) | |-------- |------- | ------------| | Env Step 0 ~ 5e5 | $724.91\pm84.05$ | $722.57\pm76.32$ | | Env Step 5e5~1e6 |$1021.42\pm721.94$ | $1806.51\pm 1340.36$ | | Env Step 1e6 ~1.5e6 | $3566.84 \pm 684.62$ | $4668.45\pm466.18$| | Env Step 1.5e6~2e6 | $4484.41\pm297.48$ | $5008.88\pm371.89$ | | HalfCheetah | f-IRL (FKL) | (Ours) PAGAR-f-IRL (FKL) | |-------- |------- | ------------| | Env Step 0 ~ 5e5 | $66.83\pm 103.92$ | $67.75\pm 97.73$| | Env Step 5e5~1e6 | $535.342\pm 316.16$ | $515.56 \pm 334.29$ | | Env Step 1e6 ~1.5e6 | $5710.97\pm 2533.63$ | $6695.71\pm3062.43$ | | Env Step 1.5e6~2e6 | $10860.97\pm667.82$ | $11195.02\pm554.76$ | | Walker2D | f-IRL (FKL) | (Ours) PAGAR-f-IRL (FKL) | |-------- |------- | ------------| | Env Step 0 ~ 5e5 |$975.37\pm178.59$ | $974.58\pm194.77$ | | Env Step 5e5~1e6 | $1442.53\pm448.38$ | $1737.44\pm781.81$ | | Env Step 1e6 ~1.5e6 | $3501.35\pm827.54$ | $3661.00\pm270.14$| | Env Step 1.5e6~2e6 | $4490.94\pm553.47$ | $4500.95\pm529.30$| Our implementation is built on the codebase provided by the authors of f-IRL. The method for combining PAGAR with f-IRL is analogous to our approach with GAIL, using the same adversarial reward learning objectives as outlined in Equations 6, 7, 32, and 34. We also use a fixed $\lambda$ parameter. For the IRL reward learning and RL policy learning process, we have directly utilized the implementation from f-IRL. Detailed explanations will be included in the revised version of the paper. [1] Ni, Tianwei, et al. "f-irl: Inverse reinforcement learning via state marginal matching." CORL 2021.

Thank You for Your Constructive Feedback and Suggestions

Dear Reviewers, We sincerely appreciate your time and effort in reviewing our submission. Your insights are invaluable, and we are grateful for the constructive feedback provided throughout this process. Your comments have significantly helped us refine and clarify our contributions. > ## 1. The Paper’s Main Idea This paper proposes reorienting Inverse Reinforcement Learning (IRL)-based Imitation Learning (IL) from data alignment to task alignment. We have worked to clearly articulate the core idea of our work, as outlined in our response: * The **goal** of our method is to learn a policy that fulfills tasks as formulated in Definition 1, which is general and encompasses many real-world examples, as demonstrated in [1] and as proved in our individual response. * We introduced the concept of task-aligned reward functions and explained our **insight** that achieving high utility under these task-aligned reward functions is essential for task fulfillment. * Our **approach** treats expert demonstrations as weak supervision to learn a set of reward functions that capture task-aligned rewards and then trains a policy to achieve high utility across this set. > ## 2. Complexity and Relevance of Benchmarks We have emphasized that the MiniGrid environment is a well-established RL benchmark with its own publication at NeurIPS 2023 [2]. Despite its seemingly simple appearance, MiniGrid presents significant challenges for current IRL/IL algorithms due to its **massive state space** (partially observable, random layout in every episode, etc.), and other complexities. We will further clarify this in our revised manuscript to address any concerns regarding the complexity of the environments we used. > ## 3. Expanded Experimental Evaluation In response to requests for more diverse and challenging experiments, we have included new experimental results, such as **continuous control tasks** and additional baselines like **f-IRL** and **RECOIL** [3]. These results demonstrate the broader applicability of our method in both online and **offline IL** and the effectiveness of our method across different types of environments, further reinforcing the robustness of our approach. > ## 4. Moving Forward We have carefully considered your feedback and will take several steps to further improve our submission. We plan to enhance the clarity of our contributions and streamline the theoretical sections to ensure that the main ideas are communicated more effectively. Additionally, we will expand our experimental results on other complex benchmarks, such as Locomujoco. Once again, we sincerely thank you for your thoughtful reviews and contributions to improving our submission. We are confident that the revisions made in response to your feedback will result in a stronger and more impactful paper. Best regards, The Authors [1] Abel, David, et al. On the expressivity of Markov reward, NeurIPS 2021 [2] Chevalier-Boisvert, Maxime, et al. “Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.” NeurIPS 2023 [3] Sikchi et al.; Dual RL: Unification and new methods for reinforcement and imitation learning, ICLR 2024