Paper:
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments.

Review:
# Paper ID 5026  Sample-efficient RL with stochastic ensemble value expansion  ## Summary The paper proposes a method to learn a memoryless policy in a sample efficient manner using an ensemble of learned MDP models, policies and Q functions. The main algorithmic idea is a weighted combination of H step temporal differences, estimated on H steps (and rolled out by a learned model of the environment). The underlying idea is to allow the learner to tradeoff between estimation errors in model and Q function in different parts of the state-action space during learning. The updated TD estimator is incorporated into the DDPG algorithm in a straightforward manner. The update is computationally more intensive but the result is improved sample complexity.  The experimental results on a variety of continuous control tasks show significant improvement over the baseline DDPG and a related method (MVE) (which is the precursor to this work).  Overall, the paper is well written. The empirical results are very promising. The analysis and discussion is a bit limited but is not a major drawback. Overall, there is much to like about the paper. Detailed comments follow.  ## Detailed Comments  - The main idea is to replace the vanilla temporal difference update with one that uses simulated trajectories to look H steps ahead (followed by the regular TD update on step H+1). The simulated trajectories are generated from an ensemble of M models. Additional ensembles of reward functions and Q functions are maintained during learning.  - My understanding is that this additional overhead (M models, N reward functions, L Q-functions) is only required during training. At test time, only the final learned Q function (and policy network) is required (right?). The paper could perhaps better describe the difference between training and test.  - In Section 3.1, I'm not sure I completely followed how the mean and variance of the TD estimates at each time step $i$ are computed. I believe the algorithm creates MNL samples on each (s, a) pair visited during training and uses the MNL samples to fit the T_i mean and variance *for that particular (s, a) pair (or (r, s') pair)*. Is this correct? I think Section 3.1 needs to be described much more clearly. A graphical visualization of the N models being rolled out and converted into the final T_i mean and variance estimates may make the learning algorithm easier to visualize.  - Assuming I've understood correctly, the proposed algorithm (STEVE) boils down to using the learned models to generate TD estimates at every step $0 \leq i \leq H$. The TD estimates are noisy (due to model errors) so the final TD estimate is a weighted combination of estimated means of the $T_i$, where the weights are larger when the variance in $T_i$ is small.  - Overall, the approach makes good intuitive sense to me. The authors provide some theoretical justification of the algorithm. A more rigorous analysis in Section 3.2 would strengthen the paper.  - The modified TD update is plugged into the DDPG RL algorithm which uses a pair of neural networks for the Q function and the policy. The paper evaluates the algorithm on a number of continuous control tasks.  - The experimental section shows that the results are quite promising. The results show the STEVE algorithm doing significantly better. The baselines are the vanilla DDPG and the MVE variant of DDPG.  - In Figure 2, I'd be interested in a discussion of the high variance. Perhaps the caption could include more details about the averaging that was performed? Additional baselines (besides DDPG) would also be good to see on the same chart.  - The ablation study description in Section 4.3 was a bit confusing. Why would the "additional parameters of the ensembles" (line 181) matter to the test-time score in Figure 3? This seems to imply that the test time score depends on the ensemble somehow but I don't think that's happening here. What am I missing?  - I found Figure 3 to be very interesting. The performance of STEVE across (H=1/3/5) was particularly interesting as well as the uniform weighting scheme. These results seem somewhat central to the claims of the method. GCP budget permitting, I think the the experimental section would be much improved if these graphs were generated for the other control tasks as well. Any preliminary results on these would be interesting to hear about as well.  - Figure 5 suggests something interesting is happening but I'm not sure I fully understand the discussion in the paper. Why exactly does the model usage go to zero in the least complex AND the most complex environments? I think it might be useful to analyze and describe these three scenarios (going to zero in easy env, going to zero in complex env, hovering around 50%) in more detail. Finally, how are the weights typically distributed across non-zero timesteps?  - Overall, I liked the paper. Additional experiments and baselines, experimental analysis and a clearer description of the algorithm would significantly strengthen the paper.   Minor points:  - I think the ensemble size ought to be mentioned somewhere in the paper and not the appendix.  UPDATE ---------- After reading the other reviews and the author responses, I'm upgrading my evaluation. I congratulate the authors on their fine work.