Title: DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
Authors: Geon-Hyeong Kim
Seokin Seo
Jongmin Lee
Wonseok Jeon
HyeongJoo Hwang
Hongseok Yang
Kee-Eung Kim
Venue: ICLR 2022
Keywords: imitation learning
offline imitation learning
imperfect demonstration
non-expert demonstration

Abstract: We consider offline imitation learning (IL), which aims to mimic the expert's behavior from its demonstration without further interaction with the environment. One of the main challenges in offline IL is to deal with the narrow support of the data distribution exhibited by the expert demonstrations that cover only a small fraction of the state and the action spaces. As a result, offline IL algorithms that rely only on expert demonstrations are very unstable since the situation easily deviates from those in the expert demonstrations. In this paper, we assume additional demonstration data of unknown degrees of optimality, which we call imperfect demonstrations. Under this setting, we propose DemoDICE, which effectively utilizes imperfect demonstrations by matching the stationary distribution of a policy with experts' distribution while penalizing its deviation from the overall demonstrations. Compared with the recent IL algorithms that adopt adversarial minimax training objectives, we substantially stabilize overall learning process by reducing minimax optimization to a direct convex optimization in a principled manner. Using extensive tasks, we show that DemoDICE achieves promising results in the offline IL from expert and imperfect demonstrations.


Review discussion:
Strengths:
-- The proposed setting is practical and important.
-- The proposed algorithm is novel (although heavily derives from existing work).
-- The empirical results & baselines are comprehensive.
-- The writing is clear and easy to follow.
Weaknesses/Questions:
-- The 3-stage process in the algorithm is arguably more complex than baselines (e.g., BC is just a single objective, ValueDICE is a single min-max objective) and can present challenges in practice w.r.t. hyperparameter tuning of each stage.
-- The derivations heavily borrow from existing work (e.g., OptiDICE, AlgaeDICE). At times, the derivations seem to be more complicated than they need to be.
For example, when introducing the log-expected-exponent, it is presented as a very involved multi-step derivation. I think it could be simplified by just noting that the Fenchel dual of KL when inputs are restricted to the simplex is exactly the log-expected-exponent function; see Section 5.3.1 in https://arxiv.org/abs/2001.01866 (in fact, this existing tutorial includes a lot of derivations similar to DemoDICE).
-- I have missed this in the paper, but I'd be curious to know how DemoDICE performs in the pure imitation learning setting (no imperfect demos) compared to the baselines.
-- You may want to include a reference to https://arxiv.org/abs/2102.13185 which also proposes an IL algorithm that is composed of a 1st stage that does discriminator-based density ratio estimation and then uses that density ratio as a reward for a later DICE-like stage.
NO.
Thank you for your constructive and thoughtful feedback.
1. [Hyperparameter tuning] DemoDICE is seemingly more complex than other baseline methods, but this is to leverage supplementary imperfect demonstrations.
Regarding implementation details, we have a comparable number of hyperparameters with ValueDICE.
First, both DemoDICE and ValueDICE have a hyperparameter $\alpha$ for KL-regularization and mixture of samples, respectively. We also point out that the performance of DemoDICE is not sensitive to the choice of $\alpha$ in our experiment reported in Appendix F.5.
Also, DemoDICE uses two regularizers for the discriminator and $\nu$ while ValueDICE uses two regularizers for $\nu$ and $\pi$.
DemoDICE has only one additional learning rate compared with ValueDICE, but we set the same learning rate for the discriminator and $\nu$.
Finally, ValueDICE and DemoDICE use neural networks with the same hidden layers (two hidden layers with 256 hidden units for each). This architecture has been widely used in many RL and IL algorithms.
2. [Derivation] We appreciate your suggestion. We revised the proof in Appendix A based on the Fenchel duality.
In summary, based on the primal and dual problem in Section 2.2 in [3.1], we derive $\tilde L(\nu;r)$ similarly to the derivation in Section 5.3.1 in [3.1].
3. [Pure offline IL] We added new experimental results for the pure offline IL setting in Appendix F.6. In Figure 8, we observe that DemoDICE and BC show similar performance.
Please note that DemoDICE does not necessarily have advantage over BC in the pure offline IL setting since they are equivalent.
In the pure offline IL, DemoDICE essentially tries to find a stationary distribution that minimizes $D_\text{KL}(d || d^E)$ on the MLE MDP constructed by $D^E$.
Bearing this in mind, the optimal solution is trivially given by $d^* = d^E$ with $D_\text{KL}(d || d^E)=0$ since $d^E$ itself is a valid stationary distribution on the MLE MDP.
Thus, extracting a policy from $d^E$ reduces to a simple behavior cloning: $\min_{\pi} - E_{(s,a) \sim d^E}[ \log \pi(a|s) ]$
4. [Related work] Thank you for your recommendation. We revised the related work section.
Off policy learning from observations (OPOLO) [3.2] addresses off-policy IL using expert demonstrations composed only of states. It has similarities to DemoDICE in two aspects.
Firstly, OPOLO adopts a discriminator to estimate a state-transition distribution ratio.
Furthermore, DICE-like technique is applied to the derivation of the objective of OPOLO, which is similar to ours.
Thus, based on the relation between DemoDICE and OPOLO, offline IL using expert demonstrations containing only states can be considered as a promising direction for future work.
[3.1] Nachum, Ofir, and Bo Dai. "Reinforcement Learning via Fenchel-Rockafellar Duality." arXiv preprint arXiv:2001.01866, 2020.
[3.2] Zhu, Zhuangdi, et al. "Off-policy Imitation Learning from Observations." NeurIPS, 2020.
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### Strengths
The proposed problem is a natural and practical extension of the offline IL problem. This is a nice step towards practical applications of IL. The derivation of the method is also grounded.
While the derivation steps are based on existing work, the stability improvement by using a soft-max surrogate and its accompanying proofs are good additions.
### Weaknesses
1) Related work
It is interesting to see that the final policy learning objective in equation 21 essentially reweighs behavioral cloning (BC) objective based on the advantage function. This result makes DemoDICE closely related to [1] which reweighs GAIL objective based on the advantage function and [2] which learns from imperfect demonstrations by reweighting a BC objective. These recent works should be discussed in the paper.
2) Experiments
Experiments are conducted sufficiently to demonstrate the effectiveness of DemoDICE on continuous-control benchmarks.
Though, there are only two baseline methods (BC and ValueDICE) and more baselines should be evaluated (e.g., the method in [2]).
3) Clarity
The paper is overall well written and well organized.
However, there are unclear statements that need further clarification:
- In Section 3.3, why cannot $w^\star$ be computed from $\tilde{\nu}^\star$? In my understanding, $\tilde{\nu}$ is a neural network that minimizes equation (18) and the advantage function can be computed directly from it. With these, it should be straightforward to compute $w^\star(s,a) = \exp(\frac{A_{\tilde{\nu}^\star}(s,a)}{1+\alpha}-1)$ on state-action samples drawn from the dataset $D^U$ to reweight the objective.
- I do not understand the sentence "Based on the weighted BC, we simply extract the policy without training any additional network.". It is indeed  possible to find a non-parametric $\pi$ from equation (21).
However, such $\pi$ only defines action probabilities on states observed in the datasets and cannot be used in the environment.
At the same time, the appendix does mention about a neural network for the actor.
So I do not understand which  "additional network" the above sentence refers to.
- Proposition 1 requires $\nu$ to be in a large family of functions. Are there restrictions of $\nu$? Lemma 1 and 2 seem to hold for any function $\nu$, so I think Proposition 1 should hold for any $\nu$.
### Questions
1) It is intriguing to see that regularized BC does not work in the experiments but DemoDICE does given that BC is also a distribution-matching approach (without matching the state-marginals). Does the superior performance of DemoDICE come from matching the state-marginals or from the dual-program optimization?
2) In theory, the policy function can be extracted from state-action distribution exactly with $\pi(a|s) = \frac{d^\pi(s,a)}{\int_A d^\pi(s,a) \text{d}a}$. The integration is intractable in general for continuous control tasks.
However, for Mujoco tasks the action-space is bounded in $[-1,1]$. Is computing the exact policy function possible in DemoDICE with the knowledge of action-space's bounds?
[1] Yunke Wang, Chang Xu, Bo Du, and Honglak Lee. Learning to Weight Imperfect Demonstrations. ICML, 2021.
[2] Fumihiro Sasaki and Ryota Yamashina. Behavioral Cloning from Noisy Demonstrations. ICLR, 2021.
NO.
We thank the reviewer for the thoughtful and detailed feedback.
1. [Related work] Thank you very much for the suggested references. We revised the related work section to incorporate the comments.
Weighted GAIL (WGAIL) [2.1] uses imperfect demonstrations for imitation learning. Inspired from the previous work [2.3], the authors suggest a general weighted GAIL objective function.
Unlike previous work, WGAIL predicts the weight for imperfect demonstrations without information about their qualities.
However, WGAIL is an online on-policy imitation learning method, whereas our algorithm, DemoDICE, is an offline imitation learning method.
Behavioral Cloning from Noisy Demonstrations (BCND) [2.2] assumes that the expert demonstrations are sampled from a noisy expert policy. BCND learns ensemble policies with a weighted BC objective, where the weight is policy learned by weighted BC in the previous internal iteration. As the learning progresses, the mode of the learned policy moves towards that of the noisy expert policy.
Thus, the learned policy tends to converge to a non-expert policy when the non-expert trajectories comprise the majority of demonstrations.
2. [Experiments] We added the BCND results in Figures 1 and 2. We use all demonstrations as noisy expert demonstrations and report the best performance among the 5 internal iterations of BCND. In Figure 1, we can observe that the normalized score results tend to decrease as we decrease the overall qualities of imperfect demonstrations (from M1 to M3), since the BCND algorithm is prone to converge to a non-expert policy when the non-expert trajectories form the most of noisy expert demonstrations, due to the nature of the aforementioned algorithm.
3. [Clarity]
(1) With $\nu^* \in \arg\min_\nu L(w_\nu^*,\nu;r)$, it is possible to compute $w^*$ directly.
However, with the surrogate objective, $\tilde\nu^* \in \arg\min_\nu \widetilde{L}(\nu;r)$, we can obtain an unnormalized ratio (i.e., $c\cdot w^*(s,a)$ for a constant $c$), which is enough to perform importance sampling with self-normalization. We revised the explanation of policy extraction
(2) Yes, we train the policy network.
However, we want to emphasize that we do not use other additional networks such as networks for $w$ as in [2.4] to learn the policy network. Thank you for pointing out the ambiguity. We revised that sentence.
(3) You are right. We revised the proposition.
Questions
1. We think that the state-marginal matching was the most crucial for the performance. The dual-program optimization also helped to obtain a stable and tractable optimization for the offline setting.
2. We could extract the policy as suggested if we had access to $d^\pi$, but it is, unfortunately, unavailable in the offline setting. The proposed integration is intractable in general.
However, we believe that the integration is amenable to importance sampling if we know the bounds of the action space.
For example, we could introduce a network $w_\phi$ and train to match $\tilde w(s,a)$.
Then, we can sample actions from $w_\phi$ using importance sampling, without resorting to the formula that requires $d^\pi$.
[2.1] Yunke Wang, Chang Xu, Bo Du, and Honglak Lee. "Learning to Weight Imperfect Demonstrations." ICML, 2021.
[2.2] Fumihiro Sasaki and Ryota Yamashina. "Behavioral Cloning from Noisy Demonstrations." ICLR, 2021.
[2.3] Wu, Yueh-Hua, et al. "Imitation Learning from Imperfect Demonstration." ICML, 2019.
[2.4] Lee, Jongmin, et al. "OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation." ICML, 2021.
The log anticipated exponent is introduced after a lengthy and convoluted multi-step derivation.
When inputs are constrained to the simplex, the Fenchel dual of KL is identical to the log-expected-exponent function (see Section 5.3.1 i for details).
https://phrazle.co
Strength: the paper studies an important problem for imitation learning with only offline data. Several tricks in equation 11, 21 helps to improve the method over baselines. Converting the original minimax problem into a minimization problem helps to stabilize the training. Comparing with BC and ValueDICE demonstrates that the proposed DemoDICE can achieve a better performance in several MuJoCo environments. Especially, it is important to have the results of different number of combinations of expert/imperfect demonstrations.
Weakness:
The experiments part may still need further work.
1. In figure 1, the half cheetah environment results seem to be unfinished. The curves are not converging yet especially for M3. It would be great to see the results when the curves converge.
2. The authors mentioned that the supplementary demonstrations are imperfect, containing expert or near-expert demonstrations.
However, the experiments section used either expert or random demonstrations. It would be great to see the performance of the algorithm when sub-optimal demonstrations are provided instead of totally random trajectories. According to proposal of the paper, the work is to tackle problems where expert demonstrations are not diverse enough. Is it possible to demonstrate how the performance of the proposed method will change when the expert demonstrations are of different coverage of the state/action space?
For example, intuitively, when the expert demonstrations cover enough space, the difference between BC, ValueDICE and DemoDICE may not be that significant, while when the expert demonstrations is of very limited coverage,DemoDICE will be much better than BC and ValueDICE.
3. In the results, it seems that the more imperfect demonstrations given the same perfect demonstrations, all methods tend to have worse performance (it appears that M1,M2,M3 are using the same number of expert demos but M3 are given much more bad demos). If that is the case, why include bad demonstrations in the data given that only using good demos can achieve a better performance?
4. In M1 task in Hopper, Walker2d, Ant, the proposed DemoDICE isn't better than the baseline BC with beta=0.
Also in the case, where the RB dataset is used, DemoDICE is not better than BC for beta=0 in ant and half cheetah. It is also worse than ValueDICE in Walker2d. Is there further explanation why this happens?
NO.
We greatly appreciate your thoughtful feedback.
1. [Convergence in HalfCheetah] We presented the converged performance of DemoDICE for HalfCheetah in Appendix F.1. DemoDICE successfully recovers the expert policy's performance at its convergence in all of HalfCheetah-M1, M2, and M3.
2. [Ablation study] We added new tasks in Appendix F.3 where random trajectories are replaced by medium trajectories, named I1, I2, and I3. Here, all tasks share the expert demonstrations composed of the single expert trajectory. Imperfect demonstrations in each task are composed of 400 expert trajectories and medium trajectories, and the number of medium trajectories is 100 for the task I1, 400 for I2, and 1600 for I3. In Figure 5, we observed that DemoDICE performs well in these new tasks.
In Appendix F.4, we added experiments with different coverages of expert demonstrations. Concretely, we constructed expert demonstrations using an increasing number of expert trajectories, namely 1, 2, 5, up to 10.  As the reviewer expected, DemoDICE is not significantly better than other baselines when the expert demonstrations cover enough space. We remind the reviewer that when the coverage is not high, DemoDICE significantly outperforms the others as shown in Figure 1 in the main text.
3. [Imperfect Demonstrations] In this paper, we aim to solve an offline IL problem with only a few expert but many non-expert or imperfect demonstrations, especially when the qualities of those imperfect demonstrations are **unknown**. Our hypothesis is that DemoDICE would perform well in such a scenario, being able to identify high-quality imperfect demonstrations automatically and exploiting them to learn a good policy. M1, M2, and M3 tasks are designed to test this hypothesis. We assume that the imitation learning agent does not know a priori the quality of each imperfect demonstration as well as the variation of the qualities of those demonstrations.
Thus, to get benefitted from these imperfect demonstrations, the agent should be able to learn how to estimate the importance of the demonstrations. This capability of the agent is tested by our tasks M1, M2, M3, which imperfect demonstrations have different variations in terms of quality.
4. [Analysis of Empirical Results]
>“In M1 task in Hopper, Walker2d, Ant, the proposed DemoDICE isn't better than the baseline BC with beta=0.”
M1 is a relatively easy task where most of the trajectories are from the expert. This explains why some baselines also succeed to achieve good performance.
>“Also in the case, where the RB dataset is used, DemoDICE is not better than BC for beta=0 in ant and half cheetah.”
| Env | All | >100 | >90 | >80 | >70 | >60 | >50 |
|-------------|------|------|-----|-----|-----|-----|-----|
| Hopper | 3514 | 0 | 117 | 176 | 217 | 281 | 385 |
| Walker2d | 1887 | 0 | 0 | 325 | 564 | 638 | 693 |
| Ant | 1318 | 333 | 479 | 576 | 629 | 672 | 727 |
| HalfCheetah | 999 | 0 | 0 | 0 | 143 | 467 | 689 |
To discuss RB tasks, we show the normalized score statistics of trajectories in the replay buffer in the table above. For each environment, we count the number of trajectories that have normalized scores greater than R as >R, so the numbers above are cumulative counts.
Thus, this table suggests a natural upper bound of the performances of imitation learning algorithms, e.g. it would be very hard to score around 100 for the HalfCheetah task.
In the Ant-RB task, we have a sufficient number of good trajectories, especially >100, so both DemoDICE and BC($\beta=0$) recover the expert policy.
In the HalfCheetah-RB task, there is no trajectory with >80, so DemoDICE eagerly distinguishes the non-expert trajectories from expert ones, neglecting non-expert demonstrations.
Overall, DemoDICE performs on par with BC($\beta=0$).
>“It is also worse than ValueDICE in Walker2d.”
When the training step equals 1M, both ValueDICE and DemoDICE show similar performances at around 80. From the statistics in the above table, since the best trajectory in RB has its normalized score less than 90, we would like to argue that both ValueDICE and DemoDICE achieve reasonable performance levels.
We thank all the reviewers for their constructive feedback and comments. The improvements and modifications in the revision are summarized below:
[Experiments]
- We added a new baseline, Behavioral Cloning from Noisy Demonstrations (BCND) [1] in Figure 1 and Figure 2.
- We constructed new tasks I1, I2, and I3 using D4RL medium-v2 instead of random trajectories similar to M1, M2, and M3, respectively. The results are in Appendix F.3.
- We added an ablation study for the coverage of expert demonstrations. The results are in Appendix F.4.
- We added pure offline IL (without imperfect demonstrations) experiments in Appendix F.6.
[Writing]
- We revised the related work section.
- We revised the proof of Proposition 1 in Appendix A based on the Fenchel duality.
- We provided a more detailed analysis of empirical results in Appendix G.
If you have any additional questions or concerns about our response, we are more than happy to answer them.
[1] Fumihiro Sasaki and Ryota Yamashina. "Behavioral Cloning from Noisy Demonstrations." ICLR, 2021.

Meta-review:
role: PC
data: comment: The paper presents a method for learning sequential decision making policies from a mix of demonstrations of varying quality. The reviewers agree, and I concur, that the method is relevant to the ICLR community. It is non-trivial, the empirical evaluations and theoretical analysis are rigorous, resulting in a novel method that produces near optimal policies from more readily available demonstrations. The authors revised the manuscript to reflect the reviewers' comments.

Decision: Accept (Poster)