Distilling Reinforcement Learning into Single-Batch Datasets

Authors: Connor Wilhelm, Dan Ventura
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: distillation, reinforcement learning, rl, meta-learning, compression

Abstract:
Dataset distillation compresses a large dataset into a small synthetic dataset such that learning on the synthetic dataset approximates learning on the large dataset. Training on the distilled dataset can be performed in as little as one step of gradient descent. We demonstrate that distillation is generalizable to different tasks by distilling reinforcement learning environments into one-batch supervised learning datasets. This demonstrates not only distillation's ability to compress a reinforcement learning task but also its ability to transform one learning modality (reinforcement learning) into another (supervised learning). We present a novel extension of proximal policy optimization for meta-learning and use it in distillation of a multi-dimensional extension of the classic cart-pole problem, all MuJoCo environments, and several Atari games. We demonstrate distillation's ability to compress complex RL environments into one-step supervised learning, explore RL distillation's generalizability across learner architectures, and demonstrate distilling an environment into the smallest-possible synthetic dataset.

Decision: reject
Reviewer scores: 3, 8, 6, 8

Reviews and discussion:
<thread>
creation date:
1730401268617

thread_type:
official_review

<thread_object>
creator: reviewer_p1g3

<content>
summary:
This paper proposes a new method / framework generalizing dataset distillation approaches to the reinforcement learning setting. A network distills the "optimal juice" from an RL environment. During test time, this distilled network can generate a dataset (input-output pairs) such that a supervised learning model trained on this dataset will get optimal rewards. Intuitively, I can think of the distilled network as trying to memorize the state-action stationary distribution of the optimal policy of an environment.

strengths:
1) I think this paper is tackling a novel problem in a novel way. Its "freshness" is definitely a strength.
2) While the results obtained right now are not ground breaking, it does open up avenues for future work.

weaknesses:
1) Missing baselines -- As far as I can tell, there are no baselines used in the paper. One fairly obvious (but could be strong) baselines is to train a PPO agent till convergence in the environment and use a diffusion model to learn the state-action distribution of the expert policy. At meta-train time, a newly generated agent can just use the state-actions generated by this model as a training dataset. 

2) Motivation -- The motivation behind why this line of work is important is not clear to me after reading the introduction. Let me elaborate -
    a) Hyper-parameter / architecture tuning - Do you believe that the hyper-parameters tuned using the distilled network will be robust when training on a new environment without distillation. It do not know, nor is there any evidence provided in the paper, that hyper-parameters / architectures that work well using distilled network will work well when training in a new RL environment from scratch.

3) Data anonymity -- I think readers might appreciate of when this is a compelling reason in the RL setting.

questions:
See Weaknesses section for points / questions to address.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730591184049

thread_type:
official_review

<thread_object>
creator: reviewer_awmk

<content>
summary:
This paper introduces a novel distillation framework for distilling online RL environments to a synthetic dataset, which allows performant agents to be trained with one-step gradient descent. It extends PPO to leverage it to produce synthetic examples. Experiments are conducted on the cart pole environment and its n-dimensional extensions. Moreover, a few atari experiments are also performed to demonstrate its generalizability.

strengths:
* Work is novel considering it is probably the first to introduce dataset distillation for online RL. However, there is a related work on dataset distillation for offline RL that is missing in related work and I believe should be discussed.(https://arxiv.org/abs/2407.20299)

* Results are promising in both Cartpole and atari experiments

* Framework does not introduce a significant overhead to the wall-time of the PPO.

weaknesses:
* One of the main issues of this paper is motivation. Dataset distillation is proposed so that a large dataset can be condensed into a synthetic smaller one however I dont think the same analogy is reasonable for RL. Firstly, when you train a supervised model with large datasets, you would get almost the same accuracy which means that this dataset would have a score equivalence given a model whereas this is not accurate for the RL environments because RL environments are not deterministic. So, RL agents' performance varies across different numbers of runs (Agarwal et al., 2021). All in all, the distilling of a single instance of an environment will only represent the data that has been generated by this specific environment, not the environment itself. I would be very surprised if the policy generated by synthetic examples will generalize to the other instances with different seeds. Lastly, the discussion of runtime and the training of 10-20k agents is problematic due to similar reasons. You should get a unique agent if all are initialized the same however RL agent would be different if the environment is not fixed. I believe the randomness of the agents is due to the randomness of initialization.



* Evaluation setup is vague, in particular in Atari. In table 2(a), you present $\textit{Average End-Episode Reward Achieved at Convergence}$. Is it the average total reward of the last 100 episodes, and also what does the st dev mean for this table, is it the average over multiple runs? I believe the evaluation setup should be clarified to interpret the given results.


* More runs and different environments(could be more atari games, or continuous control env like mujoco) are needed to demonstrate the effectiveness of the proposed method. No need for full distillation for new experiments, l4 is good enough.


* Paper is a bit hard to parse(especially section 3.1), so writing could be improved. I believe Figure 2 is redundant, does not provide any insights, and is not visually appealing. Algorithm 1 is sufficient to get the concept. Lastly, you could import booktabs package for a more used format by the ICLR papers.




 
References
- Agarwal, Rishabh et al. “Deep Reinforcement Learning at the Edge of the Statistical Precipice.” Neural Information Processing Systems (2021).

questions:
Q1) How does the policy induced by the synthetic examples preserve the trust region since the policy is not updated by the PPO loss?

Q2) Could you provide visualizations of the learned synthetic examples for 2D cartpole examples?

Q3) Do you have any insights regarding why lower ks are better even in higher dimensional cartpole settings?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730600546958

thread_type:
official_review

<thread_object>
creator: reviewer_ucbk

<content>
summary:
The paper presents an approach that distills a reinforcement learning environment into a synthetic dataset while allowing agent trained under supervised setting with limited resource to reach a comparable performance vs the direct RL training.
It also presents an generalized algorithm to control the difficulty of distillation for estimating the feasibility of the full distillation.

strengths:
Overall the paper is well-written. It provides an clearly-defined algorithm with training graph and pseudocode. 
It provides a simple algorithm based on PPO to distill RL environments into a parameterized distiller.
The performance results from an easy task to complex tasks are on par with direct RL training which demonstrates the generalizability and high distillation performance of the algorithm.

weaknesses:
* The experiments do not cover the continuous control problems which are also important part of RL environments. Demonstrating distillation on those tasks can greatly benefits to the community as many robot experiments are under continuous action space.
* If I understand correctly, the final baseline RL agent is determined by the time limit and convergence. But I would image using the same amount of training sample as in the distillation's outer loop for a more fair comparison.
* The cost saving part might be better displayed in a graph. Such as the overall time spend/number of parameter updates vs number of agent trained.

questions:
* In fig 1: What's $D_{\theta}$, same in fig 2.
* Line 197: Why is the gradient destroyed? Isn't the bound non-zero?
* Can you also clarify the instance in sec 4.2, it's the sample generated from the distiller?
* In table 2, why would some experiments did not exceed the random performance while the full distillation did?
* In fig 3, what happens to the sudden increase in (e) subplot?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730798537408

thread_type:
official_review

<thread_object>
creator: reviewer_dug4

<content>
summary:
This paper proposes a PPO-inspired dataset distillation technique.

strengths:
Distillation seems like an interesting technique to reduce the data requirement of reinforcement learning.

weaknesses:
I vote to reject primarily because the motivation for the algorithm and it's empirical evaluation is difficult to follow. I had a difficult time understanding the core takeaways of this paper. 

1. Many of the contributions listed can be combined. For instance, contributions 1, 3, 5 and 6 are essentially saying the same thing: this works propose a new distillation technique and demonstrates its effectiveness empirically. 
2. Contribution 2 doesn't seem like a contribution; it's simply a task that was created to demonstrate the distillation method. I suggest omitting. 
3. "policy gradient methods such as PPO are more sample-efficient, utilizing the entire experience memory rather than randomly sampling from it as in standard DQN learning." This sentence is unclear to me; PPO does not use experience replay, as it is an on-policy algorithm. Also, off-policy algorithms are often more sample efficient than on-policy algorithms, so this statement seems misleading.
4. First paragraph section 3.1: This paragraph motivates building off of PPO, but I think that's all that needs to be said: you build off PPO because it is a reasonably sample efficient on-policy algorithm and is often the go-to algorithm for RL applications.
4. It's unclear how the experiments extend cartpole to N dimensions. A figure for N=2 would make be informative. 
4. Table 1 is difficult to parse. What exactly are the core takeaways from this table? Why is it informative to consider different model initializations? the experiments would be easier to understand if hypotheses were stated prior to showing results -- what do we expect to see if the method works, and why?
5. If the method works, it would be useful to understand how it distills the dataset -- which samples are ultimately distilled? Can we glean any insights from it?

questions:
See weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732038110759

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Paper Revisions

comment:
In response to reviewer feedback, we have made the following changes in the latest revision:

- Performed experiments with continuous MuJoCo environments and added results to paper
- Clarified contribution 3: "Demonstrates k-shot learning on single-batch datasets distilled from ND cart-pole using various initialization distributions and architectures; demonstrating distillation’s generalization to unseen architectures"
- Changed use of "experiences," "memory," and "experience memory" to "observation sequences," "transitions," or "RL trajectories" where appropriate for more accurate description of PPO learning.
- Revise 3.1. for clarity, removed most of 1st paragraph & all references to PPO being sample-efficient
- Added figure of ND cart-pole and visualizations of 1D and 2D cart-pole distillations in appendix
- Added visualization of cost saving over number of models produced in appendix
- Removed remaining references to distilled dataset as $D\_\theta$
- Clarified evaluation procedure
- Moved Figure 2 (distillation algorithm figure) to appendix
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper develops an approach to distill the transitions sampled by PPO, to use them to initialize a new policy on a later training run. The paper implements this idea using a meta-learning-variant of PPO.

There was substantial discussion on this paper and a number of points were raised by the reviewers. The most important ones revolved around (i) evaluation methodology, (ii) subpar performance to training from scratch, (iii) motivation and (iv) baselines. The authors have satisfactorily addressed the first concern, but the others remain. This paper is poorly written, both the narrative and the mathematical rigor of the paper could be improved substantially.

This approach is quite ill-motivated, e.g., there is no generalization to new tasks or morphologies, indeed if one was able to run on the environment once, there is no more need to distill data from it. The idea of dataset distillation is about using this data for addressing new environments. The second concern is more major. There are a number of existing ideas in the RL literature, e.g., imitation learning on subsampled data, and offline RL methods in general, that could be thought of as appropriate baselines for this work. The paper has not considered any of these existing ideas. If one thinks formally about the setup in this paper, the distilled dataset is indeed the states and actions of a near-optimal PPO policy.