Paper:
In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control. Deep Q-learning, however, still suffers from poor data-efficiency which is limiting with regard to realworld applications. We follow the idea of multi-step TD-learning to enhance data-efficiency while remaining off-policy by proposing two novel TemporalDifference formulations: (1) Truncated Q-functions which represent the return for the first n steps of a target-policy rollout w.r.t. the full action-value and (2) Shifted Q-functions, acting as the farsighted return after this truncated rollout. We prove that the combination of these short-and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm. We show the efficacy of Composite Q-learning in the tabular case and compare our approach in the function-approximation setting with TD3, Model-based Value Expansion and TD3(∆), which we introduce as an off-policy variant of TD(∆). We show on three simulated robot tasks that Composite TD3 outperforms TD3 as well as state-of-the-art off-policy multi-step approaches in terms of data-efficiency.

Review:
This paper proposes the Composite Q-learning algorithm, which combines the algorithmic ideas of using compositional TD methods to truncate the horizon of the return, as well as shift a return in time. They claim that this approach will improve the method's data efficiency relative to standard Q-learning. They demonstrate its performance relative to Q-learning in a tabular domain, as well as in deep RL domains which use the compositional idea as an off-policy critic.
Overall, the paper has interesting algorithmic ideas, but there are critical issues in the evaluation and resulting claims being made. Based on this, I am recommending rejection of the paper. I do think there is value in the compositional idea, but for different reasons outlined in the suggestions.
Issues:
1) The truncation of the horizon is not a novel TD formulation, as claimed in the paper. This algorithm is described in the original TD paper (Sutton, 1988) as "prediction by a fixed interval." Sutton's group further has a recent paper following up on the fixed-horizon TD (FHTD) idea (De Asis et al., 2019), introducing an off-policy control variant of it.
2) Based on Theorem 1 of the TD(\Delta) paper (Romoff et al., 2019), as well as the sample-complexity arguments from the FHTD paper, this compositional algorithm is *exactly* equivalent to standard TD in the tabular setting (and function approximation if value functions don't share parameters), update for update, assuming that: (1) each value function is initialized identically, and (2) the same step size is used for each value function. An intuition for why is because the accuracy of the shifted action-values depends on the accuracy of the standard TD estimate, and the TD errors can be shown to exactly decompose that of standard TD. Under this, there is no ready improvement in data efficiency due to the fixed-horizon value functions converging quicker.
3) The results in the tabular setting seem to contradict what I described in Issue 2, because compositional Q-learning as presented did converge quicker than standard Q-learning. However, this is misleading in that the other methods used a step size of 1e-3, but the step size of the shifted value functions used, without explanation, a larger step size of 1e-2. The reason for the improved performance is that these values had a step size an order of magnitude larger than the remaining ones, and if one were to use the same step size across all value functions, it would have matched Q-learning exactly. This exact decomposition is supported by how the fixed-horizon value estimates follow Q-learning's curves exactly for the first h - 1 updates (and will converge to Q-learning's curve if h approaches infinity), and can further be verified by running the provided code with a step size of 1e-3 for the shifted value functions. Without acknowledging the equivalence when using a consistent step size across value functions, as well as sweeping over step sizes for each method, the results don't present a fair comparison and significantly misrepresent compositional TD methods.
4) On this observation that it is an exact decomposition of TD, it is particularly an exact decomposition of *one-step* TD, as one-step TD errors are used in the fixed-horizon and shifted value function estimates. This makes it equally biased to a one-step method, and is inconsistent with the use of "multi-step" learning in the literature where information across several time steps is included in the estimate of the return. Truncating and shifting things in time can be contextualized as a form of time-dependent discounting, and adjusting the discount rate isn't generally viewed as performing multi-step TD.
5) Based on the above, the benefit in the deep RL setting is not convincingly due to what is claimed (as parameters are shared, and a consistent step size is used in the optimizer). Some possible reasons might include the architectural choices in how the network represented the decomposition, as well as the representation learning benefits of predicting many relevant outputs to a task.
Suggestions:
1) The precise novelty of the work can be clarified, as the fixed-horizon TD formulation dates back to Sutton (1988), and has been extensively studied in De Asis et al. (2019). As far as I'm aware, there's novelty in the idea of shifting value functions, reconstructing the full return from decomposed value functions, and introducing a penalty to the loss based on inconsistencies in the value estimates.
2) The motivation and claims of the paper should be revised, as the claimed data efficiency from fixed-horizon values converging quicker isn't readily true. The resulting deep RL results may need more careful experiments to tease apart why the composition might be helping. For example, it might be useful to compare a different neural network architecture, like having all of the compositional components as outputs from the same, final hidden layer (in comparison with outputting them from intermediate hidden layers).
3) The tabular example needs to be re-worked to ensure a fair comparison between each algorithm. For example, the curves can be presented under the best step size (in terms of some metric, like area under the curve) for each algorithm. While there is an exact equivalence to standard one-step TD methods, a real benefit of the approach is that strictly more information is present to the agent, and the flexibility of being able to use separate step sizes for each value function can be favorable if it can be shown to be better after fairly tuning each algorithm. Shifting the focus toward showing that certain types of value functions are less sensitive to step sizes or work better operating at different time-scales from other components (because this seems to be what's actually happening in the results) would be a huge plus for this.
4) Because it is using one-step TD errors to estimate each of these components, and is equally biased to one-step TD, it isn't really a multi-step method. I think it would be better to emphasize the compositional aspect and its increased flexibility, than frame it as a multi-step off-policy method.
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Post-rebuttal:
I think the additional results post-discussion are good, and are on the right track of the claimed goal of analyzing the algorithm. However, the new results might be contradictory to some of the claims made earlier in the paper, and so a more involved revision seems to be needed. I do believe the algorithm has promise for the reasons teased apart in our discussion, and encourage the authors to improve their paper with these results.
To detail a few things:
1) The new results, which now empirically demonstrate the exact equivalence with one-step Q-learning, contradicts some claims about improved data efficiency due to truncated value-functions converging quicker. While meta-parameter selection isn't the focus, if the choice of meta-parameter is what can make it differ from vanilla Q-learning, and is the key explanation for the improvements, then the analysis should focus on this.
2) Mention of the equivalence only comes up in the experimental results, when it's a key property of the algorithm. If analysis of the composition is the paper's focus, acknowledging this property is foundational to any analysis of the method. It could have been shown analytically following the algorithm's derivation, and would have better justified some of the choices made in the experiments.
3) Being equivalent to running *one-step* Q-learning still makes the "multi-step" learning emphasis appear incorrect, especially when the algorithm can trivially be extended to use actual multi-step TD methods. The title seems to come from interpreting what the composite values represent, but the horizon isn't what makes a method multi-step, and the compositional components add up to exactly one-step Q-learning's update.
Minor:
1) Arguably one of the most prevalent explanations in the deep RL literature for why one might expect improvements is the multi-task/auxiliary task hypothesis (Jaderberg et al., 2016).