Title: Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Reinforcement Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Reinforcement Learning

Action Sequence

Humanoid
Abstract: Predicting a sequence of actions has been crucial in the success of recent behavior cloning algorithms in robotics. Can similar ideas improve reinforcement learning (RL)? We answer affirmatively by observing that incorporating action sequences when predicting ground-truth return-to-go leads to lower validation loss. Motivated by this, we introduce Coarse-to-fine Q-Network with Action Sequence (CQN-AS), a novel value-based RL algorithm that learns a critic network that outputs Q-values over a sequence of actions, i.e., explicitly training the value function to learn the consequence of executing action sequences. Our experiments show that CQN-AS outperforms several baselines on a variety of sparse-reward humanoid control and tabletop manipulation tasks from BiGym and RLBench.

Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Reinforcement Learning

Abstract

Predicting a sequence of actions has been crucial in the success of recent behavior cloning algorithms in robotics. Can similar ideas improve reinforcement learning (RL)? We answer affirmatively by observing that incorporating action sequences when predicting ground-truth return-to-go leads to lower validation loss. Motivated by this, we introduce Coarse-to-fine Q-Network with Action Sequence (CQNAS ), a novel value-based RL algorithm that learns a critic network that outputs Q-values over a sequence of actions, i.e., explicitly training the value function to learn the consequence of executing action sequences. Our experiments show that CQNAS outperforms several baselines on a variety of sparse-reward humanoid control and tabletop manipulation tasks from BiGym and RLBench. Code is available at: https://younggyo.me/cqn-as/

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1 Introduction

Predicting action sequences from expert trajectories is a key idea in recent successful behavior cloning (BC; Pomerleau 1988) approaches in robotics. This has enabled policies to effectively approximate the noisy, multi-modal distribution of expert demonstrations (Zhao et al., 2023; Chi et al., 2023). Can this idea similarly be useful for reinforcement learning (RL)?

Our initial finding is affirmative: we make an intriguing observation that using action sequences can enhance value learning. Specifically, with humanoid demonstrations from BiGym (Chernyadev et al., 2024), we train regression models that predict the ground-truth return-to-go, i.e., the sum of discounted future rewards from the timestep t , given the current observation and action. In Figure 2a, we find that using an action sequence a t : t + K = { a t , ..., a t + K -1 } as input results in lower validation

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losses than using a single-step action a t . We hypothesize this is because action sequences, which can correspond to behavioral primitives such as going straight , make it easier for the model to learn the long-term outcomes compared to evaluating the effect of individual single-step actions (see Appendix A for additional analysis based on a 2D Point-mass environment).

Building on this observation, we train actor-critic algorithms (Haarnoja et al., 2018; Fujimoto et al., 2018) with action sequence on stand task from HumanoidBench (Sferrazza et al., 2024). Specifically, we train the actor to output action sequence and the critic to take action sequence as inputs instead of single-step actions. However, we find these algorithms with action sequences suffer from severe value overestimation (see Figure 2b) and completely fail to solve the task. This is because a wider action space makes the critic more vulnerable to function approximation error (Fujimoto et al., 2018) and the actor excessively maximizes value functions by exploiting this estimation error. To further support this, in Figure 2c, we report additional toy experiments where we introduce redundant no-op actions for training TD3 agents on Cheetah Run task (Tassa et al., 2020). Here, we find that actor-critic algorithms are indeed vulnerable to value overestimation with high-dimensional action spaces.

This result motivates us to design our RL algorithm with action sequence upon a recent critic-only algorithm, i.e., Coarse-to-fine Q-Network (CQN; Seo et al. 2024), which solves continuous control tasks with discrete actions. Because there is no separate actor that may exploit value functions, i.e., CQN simply selects discrete actions with the highest Q-values, we find that training with action sequences is stable and thus avoids value overestimation problem (see Figure 2c). In particular, we introduce Coarse-to-fine Q-Network with A ction S equence (CQNAS ), which learns a critic network that outputs Q-values over a sequence of actions (see Figure 3). By training the critic network to explicitly learn the consequence of taking a sequence of current and future actions, CQNAS enables the RL agents to effectively learn useful value functions on challenging robotic tasks.

Our experiments show that CQNAS improves the performance of CQN on sparse-reward humanoid control tasks from BiGym benchmark (Chernyadev et al., 2024) that provides human-collected demonstrations and sparse-reward tabletop manipulation tasks from RLBench (James et al., 2020) that provide demonstrations generated via motion-planning. Considering that CQNAS is a critic-only algorithm that selects actions with the highest Q-value without a separate actor network, these results highlight the benefit of using action sequences in value learning.

Our contributions can be summarized as below:

Wemakeanobservation that shows using action sequences can be useful for RL by enhancing value learning. We also show that standard actor-critic algorithms (Haarnoja et al., 2018; Fujimoto et al., 2018) suffer from value overestimation when trained with action sequences.

We introduce Coarse-to-fine Q-Network with Action Sequence (CQNAS ) that trains a critic network to output Q-values over action sequences. This critic-only algorithm successfully avoids value overestimation problem and enhances the base CQN algorithm.

Sequence Step 1

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(a) Coarse-to-fine inference procedure

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[

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In a demo-driven RL setup that initializes training with expert demonstrations, we show that CQNAS surpasses the performance of ACT (Zhao et al., 2023) - a BC algorithm that trains a Transformer (Vaswani et al., 2017) to predict action sequences.

2 Preliminaries

We formulate a robotic control problem as a partially observable Markov decision process (Kaelbling et al., 1998; Sutton & Barto, 2018). At time step t , an RL agent encounters an observation o t , executes an action a t , receives a reward r t +1 , and encounters a new observation o t +1 from the environment. We aim to train a policy π that maximizes the expected sum of rewards through RL while using as few online samples as possible, optionally with access to a modest amount of expert demonstrations.

Inputs and encoding Given visual observations o v t = { o v 1 t , ..., o v M t } from M cameras, we encode each o v i t using convolutional neural networks (CNN) into h v i t . We then process them through a series of linear layers to fuse them into h v t . If low-dimensional observations o low t are available along with visual observations, we process them through a series of linear layers to obtain h low t . We then use concatenated features h t = [ h v t , h low t ] as inputs to the critic network. In domains without vision sensors, we simply use o low t as h t without encoding the low-dimensional observations.

Coarse-to-fine Q-Network Coarse-to-fine Q-Network (CQN; Seo et al. 2024) is a critic-only RL algorithm that solves continuous control tasks with discrete actions. CQN trains an RL agent to learn to select coarse discrete actions in shallower levels with larger bin sizes, and then refine their choices by selecting finer-grained actions in deeper levels with smaller bin sizes. Specifically, CQN iterates the procedures of (i) discretizing the continuous action space into multiple bins and (ii) selecting the bin with the highest Q-value to further discretize. This reformulates the continuous control problem as a multi-level discrete control problem, allowing for the use of ideas from sample-efficient discrete RL algorithms (Mnih et al., 2015; Silver et al., 2017) for continuous control.

Formally, let a l t be an action at level l with a 0 t being the zero vector. 1 We then define the coarse-to-fine critic to consist of multiple Q-networks which compute Q-values for actions at each level a l t , given the features h t and actions from the previous level a l -1 t , as follows:

where a l,b t denotes an action for each bin b and B is the number of bins for each level. We note that CQN uses scalar values representing the center of each bin for previous level's action a l -1 t , enabling the network to locate itself without access to all previous levels' actions. We optimize each Q-network at level l with the following objective:

where ¯ θ are delayed parameters for a target network (Polyak & Juditsky, 1992) and π l is a policy that outputs the action a l t at each level l via the inference steps with our critic, i.e., π l ( h t ) = a l t . Specifically, to output actions at time step t , CQN first initializes constants a low t and a high t with -1 and 1 . Then the following steps are repeated for l ∈ { 1 , ..., L } :

Step 1 (Discretization): Discretize an interval [ a low t , a high t ] into B uniform intervals, and each of these intervals become an action space for Q l θ .

Step 2 (Bin selection): Find a bin with the highest Q-value and set a l t to the centroid of the bin.

Step 3 (Zoom-in): Set a low t and a high t to the minimum and maximum of the selected bin, which intuitively can be seen as zooming-into each bin.

We then use the last level's action a L t as the action at time step t . For more details, including the inference procedure for computing Q-values, we refer readers to Appendix C.

3 Method

We present Coarse-to-fine Q-Network with A ction S equence (CQNAS ), a value-based RL algorithm that learns a critic network that outputs Q-values for a sequence of actions a t : t + K = { a t , ..., a t + K -1 } for a given observation o t . Our main motivation comes from one of the key ideas in recent BC approaches: predicting action sequences , which helps resolve ambiguity when approximating noisy distributions of expert demonstrations (Zhao et al., 2023; Chi et al., 2023). Similarly, by explicitly learning Q-values of a sequence of actions from the given state, our approach mitigates the challenge of learning Q-values with noisy trajectories. We provide the overview of CQNAS in Figure 3.

3.1 Coarse-to-fine Critic with Action Sequence

Objective Let a l t : t + K = { a l t , ..., a l t + K -1 } be an action sequence at level l and a 0 t : t + K be a zero vector. We design our coarse-to-fine critic network to consist of multiple Q-networks that compute Q-values for each action at sequence step k ∈ { 1 , ..., K } and level l ∈ { 1 , ..., L } :

where a l,b t + k -1 denotes an action for each bin b at step k . We optimize our critic network with the following objective:

where N is a hyperparameter for N -step return and π l K is an action sequence policy that outputs the action sequence a l t : t + K by following the similar inference procedure as in Section 2 (see Figure 3a). In practice, we compute Q-values for all sequence step k ∈ { 1 , ..., K } in parallel, which is possible as Q-values for future actions depend only on features h t but not on previous actions.

Remarks on objective with N -step return We note that any N -step return can be used in Equation 2 because the network can learn the long-term value of outputting action a t + k from bootstrapping. There is a trade-off: if one considers a short N -step return, it can cause a challenge as the setup becomes a delayed reward setup; but training with higher N -step return may introduce variance (Sutton & Barto, 2018). In our considered setups, we empirically find that using common values N ∈ { 1 , 4 } works the best. We provide empirical analysis on the effect of N in Figure 8c.

Architecture Our critic network initially extracts features for each sequence step k and aggregates features from multiple steps with a recurrent network (see Figure 3b). This architecture is helpful in cases where a single-step action is already high-dimensional so that concatenating them make inputs too high-dimensional. Specifically, let e k denote an one-hot encoding for k . At each level l , we construct features for each sequence step k as h l t,k = [ h t , a l -1 t + k -1 , e k ] . We then encode each h l t,k with a shared MLP network and process them through GRU (Cho et al., 2014) to obtain s l t,k = f GRU θ ( f MLP θ ( h l t, 1 ) , ..., f MLP θ ( h l t,k ) . We find that this design empirically performs better than directly giving actions as inputs to GRU. We then use a shared projection layer to map each s l t,k into Q-values at each sequence step k , i.e., [ Q l,k θ ( h t , a l,b t + k -1 , a l -1 t : t + K )] B b =1 = f proj θ ( s l t,k ) .

3.2 Action Execution and Training Details

Executing action with temporal ensemble With the policy that outputs an action sequence a t : t + K , one question is how to execute actions at time step i ∈ { t, ..., t + K -1 } . For this, we use temporal ensemble (Zhao et al., 2023) that computes a t : t + K every time step, saves it to a buffer, and executes a weighted average ∑ i w i ¯ a i t / ∑ w i where ¯ a i t denotes an action for step t computed at step t -i , w i = exp( -m ∗ i ) denotes a weight that assigns higher value to more recent actions, with m as a hyperparameter that adjusts the weighting magnitude. We find this scheme outperforms the alternative of computing a t : t + K every K steps and executing each action for subsequent K steps on most tasks we considered, except on several tasks that need reactive control.

Storing training data When storing samples from the environment, we store a transition ( o t , ˆ a t , r t +1 , o t +1 ) where ˆ a t denotes an action executed at time step t . For instance, if we use temporal ensemble for action execution, ˆ a t is a weighted average of action outputs obtained from previous K time steps, i.e., ˆ a t = ∑ i w i ¯ a i t / ∑ w i .

Sampling training data from a replay buffer When sampling training data from the replay buffer, we sample a transition with action sequence, i.e., ( o t , ˆ a t : t + K , r t +1 , o t +1 ) . If we sample time step t near the end of episode so that we do not have enough data to construct a full action sequence, we fill the action sequence with null actions. In particular, in position control where we specify the position of joints or end effectors, we repeat the action from the last step so that the agent learns not to change the position. In torque control where we specify the force to apply, we set the action after the last step to zero so that agent learns to not to apply force.

4 Experiment

We study CQNAS on 25 humanoid control tasks from BiGym (Chernyadev et al., 2024) and 20 tabletop manipulation tasks from RLBench (James et al., 2020) (see Figure 4 for examples of robotic tasks). To focus on challenging robotic tasks that aim to induce policies generating realistic behaviors, we consider a practical setup of demo-driven RL where we initialize training with a modest amount of expert demonstrations and then train with online data. In particular, our experiments are designed to investigate the following questions:

Can CQNAS quickly match the performance of a recent BC algorithm (Zhao et al., 2023) and surpass it through online learning? How does CQNAS compare to previous model-free RL algorithms (Yarats et al., 2022; Seo et al., 2024)?

What is the effect of each component in CQNAS ?

Under which conditions is CQNAS effective?

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Baselines We consider model-free RL baselines that learn deterministic policies, as we find that stochastic policies struggle to solve fine-grained manipulation tasks. Specifically, we consider (i) Coarse-to-fine Q-Network (CQN; Seo et al. 2024), our backbone algorithm and (ii) DrQ-v2+, an optimized demo-driven variant of an actor-critic algorithm DrQ-v2 (Yarats et al., 2022) that uses a deterministic policy algorithm and data augmentation. We further consider (iii) Action Chunking Transformer (ACT; Zhao et al. 2023) that trains a transformer (Vaswani et al., 2017) policy to predict action sequences from expert demonstrations and utilizes temporal ensemble, as our BC baseline.

Implementation details For training with expert demonstrations, we follow the setup of Seo et al. (2024). We keep a separate replay buffer that stores demonstrations and sample half of training data from demonstrations. We also relabel successful online episodes as demonstrations and store them in the demonstration replay buffer. For CQNAS , we use an auxiliary BC loss from Seo et al. (2024) based on large margin loss (Hester et al., 2018). For actor-critic baselines, we use an auxiliary BC loss that minimizes L2 loss between the policy outputs and expert actions.

4.1 BiGym Experiments

We study CQNAS on mobile bi-manual manipulation tasks from BiGym (Chernyadev et al., 2024). BiGym's human-collected demonstrations are often noisy and multi-modal, posing challenges for RL algorithms. These algorithms must effectively leverage the information within demonstrations to learn strong initial behaviors, thereby mitigating exploration difficulties in sparsely rewarded tasks.

Setup We consider 25 BiGym tasks with 17 to 60 demonstrations 2 . We use RGB observations with 84 × 84 resolution from head , left_wrist , and right_wrist cameras. We also use lowdimensional proprioceptive states. We use (i) absolute joint position control action mode and (ii)

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floating base that replaces locomotion with classic controllers. We use the same set of hyperparameters for all the tasks. Details on BiGym experiments are available in Appendix B.

Comparison to baselines Figure 5 shows that CQNAS quickly matches the performance of ACT and outperforms it through online learning on most tasks, while other RL algorithms fail to do so especially on challenging long-horizon tasks such as Move Plate and Saucepan To Hob . A notable result here is that CQNAS enables solving challenging BiGym tasks while other RL baselines completely fail as they achieve a 0% success rate on many tasks.

Limitation However, CQNAS struggles to achieve meaningful success rate on some of the longhorizon tasks that require interaction with delicate objects such as cups or cutlery. This leaves room for future work to incorporate advanced vision encoders (He et al., 2016) or critic architectures (Chebotar et al., 2023; Springenberg et al., 2024).

4.2 RLBench Experiments

We also study CQNAS on manipulation tasks from RLBench (James et al., 2020). Unlike BiGym, RLBench provides synthetic demonstrations generated via motion planning, which are cleaner and more consistent. This allows us to examine whether CQNAS is also effective in settings with clean , unambiguous demonstrations - where the effect of each single-step action is easier to interpret.

Setup We use the official CQN implementation for collecting demonstrations and reproducing the baseline results on the same set of tasks. We use RGB observations with 84 × 84 resolution from front , wrist , left_shoulder , and right_shoulder cameras. We also use low-dimensional proprioceptive states consisting of 7-dimensional joint positions and a binary value for gripper open. We use 100 demonstrations and delta joint position control action mode. We use the same set of hyperparameters for all the tasks, in particular, we use action sequence of length 4. More details on RLBench experiments are available in Appendix B.

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CQN-AS is also effective with clean demonstrations Because RLBench provides synthetic clean demonstrations, as we expected, Figure 6 shows that CQNAS achieves similar performance to CQN on most tasks, except 2/25 tasks where it hurts the performance. But we still find that CQNAS achieves quite superior performance to CQN on some challenging long-horizon tasks such as Open Oven or Take Plate Off Colored Dish Rack . These results show that CQNAS can be used in various benchmark with different characteristics.

4.3 HumanoidBench Experiments

To show that CQNAS is generally applicable to tasks without demonstrations, we also study CQNAS on densely-rewarded tasks from HumanoidBench (Sferrazza et al., 2024).

Setup We follow a standard setup that trains RL agents from scratch. We use low-dimensional states consisting of proprioception and privileged task information as inputs. For tasks, we simply select the first 8 locomotion tasks in the benchmark. For baselines, we consider CQN and Soft Actor-Critic (SAC) (Haarnoja et al., 2018). For SAC, we use the results available from HumanoidBench repository, which are evaluated on tasks with dexterous hands . For CQNAS and CQN, we also evaluate them on tasks with hands. We use the same set of hyperparameters for all the tasks (see Appendix B).

Comparison to baselines Figure 7 shows that, by learning the critic network with action sequence, CQNAS outperforms other model-free RL baselines, i.e., CQNand SAC, on most tasks. In particular, the difference between CQNAS and baselines becomes larger as the task gets more difficult, e.g., baselines fail to achieve high episode return on Walk and Run tasks but CQNAS achieves strong performance. This result shows that our idea of using action sequence can be applicable to generic setup without demonstrations.

4.4 Ablation Studies, Analysis, Failure Cases

Effect of action sequence length Figure 8a shows the performance of CQNAS with different action sequence lengths. We find that training the critic network with longer action sequences tends to consistently improve performance, plateaus or decreases performance if the sequences get too long.

RL objective is crucial for strong performance Figure 8b shows the performance of CQNAS without RL objective that trains the model only with BC objective on successful demonstrations. We find this baseline significantly underperforms CQNAS , which shows that RL objective enables the agent to learn from trial-and-error experiences.

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Effect of N -step return Figure 8c shows experimental results with varying N -step returns. We find that too high N -step return significantly degrades performance. We hypothesize this is because the variance from N -step return makes it difficult to learn useful value functions.

Effect of temporal ensemble Figure 8d shows that performance degrades without temporal ensemble on Saucepan To Hob as temporal ensemble induces a smooth motion and thus improves performance in fine-grained control tasks. But we also find that temporal ensemble is harmful on Reach Target Single . This is because temporal ensemble uses predictions from previous steps and thus makes it difficult to refine behaviors based on recent observations. Nonetheless, we use temporal ensemble for all the tasks as it helps on most tasks and we aim to use the same set of hyperparameters.

Effect of temporal ensemble magnitude We further provide results with different temporal ensemble magnitudes by adjusting a hyperparameter m in Figure 8e. Here, higher m puts higher weights on recent actions and thus very high m corresponds to using only first action. Similarly to previous paragraph, we find that higher m leads to better performance on Reach Target Single that needs fast reaction, but degrades performance on Saucepan To Hob .

Failure case: Torque control Figure 8f shows that CQNAS underperforms CQN on locomotion tasks with torque control, which are drawn from the DeepMind Control Suite (Tassa et al., 2020). We hypothesize that this performance degradation arises because sequences of joint positions tend to have clearer semantic structure in joint space, making them easier to learn from compared to sequences of raw torques. Addressing this failure case represents an interesting direction for future work.

5 Related Work

Behavior cloning with action sequence Recent behavior cloning approaches have shown that predicting a sequence of actions enables the policy to imitate noisy expert trajectories and helps in dealing with idle actions from human pauses during data collection (Zhao et al., 2023; Chi et al., 2023). Notably, Zhao et al. (2023) train a transformer model (Vaswani et al., 2017) that predicts action sequence and Chi et al. (2023) train a denoising diffusion model (Ho et al., 2020) that approximates the action distributions. This idea has been extended to multi-task setup (Bharadhwaj et al., 2024), mobile manipulation (Fu et al., 2024b) and humanoid control (Fu et al., 2024a). Our work is inspired by this line of research and proposes to learn RL agents with action sequence.

Reinforcement learning with action sequence In the context of RL, Medini & Shrivastava (2019) propose to pre-compute frequent action sequences from expert demonstrations and augment the action space with these sequences. However, this idea introduces additional complexity and is not scalable to setups without demonstrations. One recent work relevant to ours is Saanum et al. (2024) that encourage a sequence of actions from RL agents to be predictable and smooth. Our work differs in that we directly incorporate action sequences into value learning. Recently, Ankile et al. (2024) point out that RL with action sequence is challenging and instead propose to use RL for learning a single-step policy that corrects action sequence predictions from BC. In contrast, we show that RL with action sequence is feasible and improves performance of RL algorithms.

Multi-token prediction Recent large language models have incorporated a notably similar idea to predicting action sequences from demonstrations - predicting multiple future tokens at once, or multi-token prediction (Gloeckle et al., 2024; Liu et al., 2024). For instance, Gloeckle et al. (2024) show that predicting multiple n future tokens in parallel with n independent output heads improves the performance and can accelerate inference speed. DeepSeek-V3 (Liu et al., 2024) also make a similar observation but with a sequential multi-token prediction. It would be interesting to see whether our idea can be utilized for fine-tuning these models with multi-token prediction.

Hierarchical reinforcement learning Approaches that learn RL agents with temporally extended high-level actions, or options, have been well studied (Sutton, 1988). The key idea is to train highlevel policies that output options by manually defining subgoals (Kulkarni et al., 2016; Dayan & Hinton, 1992) or learning options from data (Bacon et al., 2017; Vezhnevets et al., 2017; Nachum et al., 2018), and then train a low-level agent that learns to execute low-level actions conditioned on options. Our work is not directly comparable to these works as we do not abstract temporally extended actions but use raw action sequences for value learning.

6 Discussion

We have presented Coarse-to-fine Q-Network with A ction S equence (CQNAS ), a critic-only RL algorithm that trains a critic network to output Q-values over action sequences. Extensive experiments in benchmarks with various setups show that our idea not only improves the performance of the base algorithm but also allows for solving complex tasks where prior RL algorithms fail.

Limitations and future work One limitation of our work is the lack of real-world robot evaluation. Moreover, as discussed in Section 4.1 and Section 4.4, solving tasks involving small objects remains a limitation of our approach. One potential approach would be using strong pre-trained vision encoders, but we find that computational cost is often prohibitively large, which remains as an open problem. We are excited about future directions, including real-world RL with humanoid robots, incorporating advanced critic architectures (Kapturowski et al., 2023; Chebotar et al., 2023; Springenberg et al., 2024), bootstrapping RL agents from imitation learning (Hu et al., 2023; Xing et al., 2024) or offline RL (Nair et al., 2020; Lee et al., 2021), extending the idea to recent model-based RL approaches (Hafner et al., 2023; Hansen et al., 2024), extend parallel value learning scheme to autoregressive, multi-step Q-learning scheme (Kahn et al., 2018), fine-tuning vision-language-action models that use action sequence (Team et al., 2024; Doshi et al., 2024) or language models that use multi-token prediction (Gloeckle et al., 2024; Liu et al., 2024) with our algorithm, to name but a few.

References

Ankile, Lars, Simeonov, Anthony, Shenfeld, Idan, Torne, Marcel, and Agrawal, Pulkit. From imitation to refinement-residual rl for precise visual assembly. arXiv preprint arXiv:2407.16677 , 2024.

Ba, Jimmy Lei, Kiros, Jamie Ryan, and Hinton, Geoffrey E. Layer normalization. arXiv preprint arXiv:1607.06450 , 2016.

Bacon, Pierre-Luc, Harb, Jean, and Precup, Doina. The option-critic architecture. In Proceedings of the AAAI conference on artificial intelligence , 2017.

Bellemare, Marc G, Dabney, Will, and Munos, Rémi. A distributional perspective on reinforcement learning. In International Conference on Machine Learning , 2017.

Bharadhwaj, Homanga, Vakil, Jay, Sharma, Mohit, Gupta, Abhinav, Tulsiani, Shubham, and Kumar, Vikash. Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking. In 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024.

Chebotar, Yevgen, Vuong, Quan, Hausman, Karol, Xia, Fei, Lu, Yao, Irpan, Alex, Kumar, Aviral, Yu, Tianhe, Herzog, Alexander, Pertsch, Karl, et al. Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions. In Conference on Robot Learning , 2023.

Chernyadev, Nikita, Backshall, Nicholas, Ma, Xiao, Lu, Yunfan, Seo, Younggyo, and James, Stephen. Bigym: A demo-driven mobile bi-manual manipulation benchmark. In Conference on Robot Learning , 2024.

Chi, Cheng, Feng, Siyuan, Du, Yilun, Xu, Zhenjia, Cousineau, Eric, Burchfiel, Benjamin, and Song, Shuran. Diffusion policy: Visuomotor policy learning via action diffusion. In Robotics: Science and Systems , 2023.

Cho, Kyunghyun, Van Merriënboer, Bart, Gulcehre, Caglar, Bahdanau, Dzmitry, Bougares, Fethi, Schwenk, Holger, and Bengio, Yoshua. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 , 2014.

Dayan, Peter and Hinton, Geoffrey E. Feudal reinforcement learning. Advances in neural information processing systems , 1992.

Doshi, Ria, Walke, Homer, Mees, Oier, Dasari, Sudeep, and Levine, Sergey. Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation. In Conference on Robot Learning , 2024.

Fu, Zipeng, Zhao, Qingqing, Wu, Qi, Wetzstein, Gordon, and Finn, Chelsea. Humanplus: Humanoid shadowing and imitation from humans. In Conference on Robot Learning , 2024a.

Fu, Zipeng, Zhao, Tony Z, and Finn, Chelsea. Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation. In Conference on Robot Learning , 2024b.

Fujimoto, Scott, Hoof, Herke, and Meger, David. Addressing function approximation error in actor-critic methods. In International Conference on Machine Learning , 2018.

Gloeckle, Fabian, Idrissi, Badr Youbi, Rozière, Baptiste, Lopez-Paz, David, and Synnaeve, Gabriel. Better & faster large language models via multi-token prediction. arXiv preprint arXiv:2404.19737 , 2024.

Haarnoja, Tuomas, Zhou, Aurick, Hartikainen, Kristian, Tucker, George, Ha, Sehoon, Tan, Jie, Kumar, Vikash, Zhu, Henry, Gupta, Abhishek, Abbeel, Pieter, et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905 , 2018.

Hafner, Danijar, Pasukonis, Jurgis, Ba, Jimmy, and Lillicrap, Timothy. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104 , 2023.

Hansen, Nicklas, Su, Hao, and Wang, Xiaolong. Td-mpc2: Scalable, robust world models for continuous control. In International Conference on Learning Representations , 2024.

He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition , 2016.

Hendrycks, Dan and Gimpel, Kevin. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 , 2016.

Hester, Todd, Vecerik, Matej, Pietquin, Olivier, Lanctot, Marc, Schaul, Tom, Piot, Bilal, Horgan, Dan, Quan, John, Sendonaris, Andrew, Osband, Ian, et al. Deep q-learning from demonstrations. In Proceedings of the AAAI conference on artificial intelligence , 2018.

Ho, Jonathan, Jain, Ajay, and Abbeel, Pieter. Denoising diffusion probabilistic models. Advances in neural information processing systems , 2020.

Hu, Hengyuan, Mirchandani, Suvir, and Sadigh, Dorsa. Imitation bootstrapped reinforcement learning. arXiv preprint arXiv:2311.02198 , 2023.

James, Stephen, Freese, Marc, and Davison, Andrew J. Pyrep: Bringing v-rep to deep robot learning. arXiv preprint arXiv:1906.11176 , 2019.

James, Stephen, Ma, Zicong, Arrojo, David Rovick, and Davison, Andrew J. Rlbench: The robot learning benchmark & learning environment. IEEE Robotics and Automation Letters , 5(2):30193026, 2020.

Kaelbling, Leslie Pack, Littman, Michael L, and Cassandra, Anthony R. Planning and acting in partially observable stochastic domains. Artificial intelligence , 1998.

Kahn, Gregory, Villaflor, Adam, Ding, Bosen, Abbeel, Pieter, and Levine, Sergey. Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation. In 2018 IEEE international conference on robotics and automation (ICRA) , 2018.

Kapturowski, Steven, Campos, Víctor, Jiang, Ray, Raki´ cevi´ c, Nemanja, van Hasselt, Hado, Blundell, Charles, and Badia, Adrià Puigdomènech. Human-level atari 200x faster. In International Conference on Learning Representations , 2023.

Kulkarni, Tejas D, Narasimhan, Karthik, Saeedi, Ardavan, and Tenenbaum, Josh. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. Advances in neural information processing systems , 29, 2016.

Lee, Seunghyun, Seo, Younggyo, Lee, Kimin, Abbeel, Pieter, and Shin, Jinwoo. Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble. In Conference on Robot Learning , 2021.

Liu, Aixin, Feng, Bei, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437 , 2024.

Loshchilov, Ilya and Hutter, Frank. Decoupled weight decay regularization. In International Conference on Learning Representations , 2019.

Medini, Tharun and Shrivastava, Anshumali. Mimicking actions is a good strategy for beginners: Fast reinforcement learning with expert action sequences, 2019. URL https://openreview. net/forum?id=HJfxbhR9KQ .

Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A, Veness, Joel, Bellemare, Marc G, Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K, Ostrovski, Georg, et al. Humanlevel control through deep reinforcement learning. Nature , 2015.

Nachum, Ofir, Gu, Shixiang Shane, Lee, Honglak, and Levine, Sergey. Data-efficient hierarchical reinforcement learning. Advances in neural information processing systems , 2018.

Nair, Ashvin, Gupta, Abhishek, Dalal, Murtaza, and Levine, Sergey. Awac: Accelerating online reinforcement learning with offline datasets. arXiv preprint arXiv:2006.09359 , 2020.

Nakamoto, Mitsuhiko, Zhai, Simon, Singh, Anikait, Sobol Mark, Max, Ma, Yi, Finn, Chelsea, Kumar, Aviral, and Levine, Sergey. Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning. Advances in Neural Information Processing Systems , 2023.

Polyak, Boris T and Juditsky, Anatoli B. Acceleration of stochastic approximation by averaging. SIAM journal on control and optimization , 1992.

Pomerleau, Dean A. Alvinn: An autonomous land vehicle in a neural network. In Advances in neural information processing systems , 1988.

Rohmer, Eric, Singh, Surya PN, and Freese, Marc. V-rep: A versatile and scalable robot simulation framework. In IEEE/RSJ international conference on intelligent robots and systems , 2013.

Saanum, Tankred, Éltet˝ o, Noémi, Dayan, Peter, Binz, Marcel, and Schulz, Eric. Reinforcement learning with simple sequence priors. Advances in Neural Information Processing Systems , 2024.

Seo, Younggyo, Uruç, Jafar, and James, Stephen. Continuous control with coarse-to-fine reinforcement learning. In Conference on Robot Learning , 2024.

Seyde, Tim, Werner, Peter, Schwarting, Wilko, Gilitschenski, Igor, Riedmiller, Martin, Rus, Daniela, and Wulfmeier, Markus. Solving continuous control via q-learning. In International Conference on Learning Representations , 2023.

Sferrazza, Carmelo, Huang, Dun-Ming, Lin, Xingyu, Lee, Youngwoon, and Abbeel, Pieter. Humanoidbench: Simulated humanoid benchmark for whole-body locomotion and manipulation. In Robotics: Science and Systems , 2024.

Silver, David, Schrittwieser, Julian, Simonyan, Karen, Antonoglou, Ioannis, Huang, Aja, Guez, Arthur, Hubert, Thomas, Baker, Lucas, Lai, Matthew, Bolton, Adrian, et al. Mastering the game of go without human knowledge. nature , 2017.

Springenberg, Jost Tobias, Abdolmaleki, Abbas, Zhang, Jingwei, Groth, Oliver, Bloesch, Michael, Lampe, Thomas, Brakel, Philemon, Bechtle, Sarah, Kapturowski, Steven, Hafner, Roland, et al. Offline actor-critic reinforcement learning scales to large models. In International Conference on Machine Learning , 2024.

Sutton, Richard S. Learning to predict by the methods of temporal differences. Machine learning , 1988.

Sutton, Richard S and Barto, Andrew G. Reinforcement learning: An introduction . MIT press, 2018.

Tassa, Yuval, Tunyasuvunakool, Saran, Muldal, Alistair, Doron, Yotam, Liu, Siqi, Bohez, Steven, Merel, Josh, Erez, Tom, Lillicrap, Timothy, and Heess, Nicolas. dm_control: Software and tasks for continuous control. arXiv preprint arXiv:2006.12983 , 2020.

Team, Octo Model, Ghosh, Dibya, Walke, Homer, Pertsch, Karl, Black, Kevin, Mees, Oier, Dasari, Sudeep, Hejna, Joey, Kreiman, Tobias, Xu, Charles, et al. Octo: An open-source generalist robot policy. In Robotics: Science and Systems , 2024.

Todorov, Emanuel, Erez, Tom, and Tassa, Yuval. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012.

Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N, Kaiser, Łukasz, and Polosukhin, Illia. Attention is all you need. In Advances in Neural Information Processing Systems , 2017.

Vezhnevets, Alexander Sasha, Osindero, Simon, Schaul, Tom, Heess, Nicolas, Jaderberg, Max, Silver, David, and Kavukcuoglu, Koray. Feudal networks for hierarchical reinforcement learning. In International conference on machine learning , 2017.

Wang, Ziyu, Schaul, Tom, Hessel, Matteo, Hasselt, Hado, Lanctot, Marc, and Freitas, Nando. Dueling network architectures for deep reinforcement learning. In International conference on machine learning , 2016.

Xing, Jiaxu, Romero, Angel, Bauersfeld, Leonard, and Scaramuzza, Davide. Bootstrapping reinforcement learning with imitation for vision-based agile flight. In Conference on Robot Learning , 2024.

Yarats, Denis, Fergus, Rob, Lazaric, Alessandro, and Pinto, Lerrel. Mastering visual continuous control: Improved data-augmented reinforcement learning. In International Conference on Learning Representations , 2022.

Zhao, Tony Z, Kumar, Vikash, Levine, Sergey, and Finn, Chelsea. Learning fine-grained bimanual manipulation with low-cost hardware. In Robotics: Science and Systems , 2023.

A Motivating Experiments

Additional details For Figure 2a, we use the same demonstrations used in our main experiments (see Appendix B for more details). For SAC and TD3 experiments with action sequences in Figure 2b, we implemented our code based on the official HumanoidBench repository. We use the hyperparameters in the repository for training SAC agents. For TD3, we use the standard deviation of 0.2 for exploration. We report the average target Q-values recorded throughout experiments. For Figure 2c, we train SAC and CQN agents with 6 original actions and 294 no-op actions with [-1, 1] action bounds and use an environment wrapper that slices out no-op actions.

Experiment with 2D Point-mass environment To further motivate the use of action sequence for value learning, we train DQN agents (Mnih et al., 2015) on 2D Point-mass environment with discrete action spaces. In particular, we train a Raw agent that trains with the raw discrete action space consisting of single-step accelerations parameterized by 8 discrete headings (cardinal and discrete directions) and 1 magnitude level, resulting in 8 total actions. We compare this against a Sequence agent that trains with the discrete action space that consists of smooth 5-step acceleration sequences parameterized by cubic Bezier curves instead of single-step accelerations. Both agents operate on a 2D double-integrator environment where the goal is to reach a target position. In Figure 9, as expected, we find that training DQN agent with pre-defined action sequences lead to faster convergence. Our main experimental results in Section 4 further show that, CQN-AS can achieve similar benefit of using action sequences without pre-defined set of action sequences on various challenging continuous control benchmarks.

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B Experimental Details

BiGym BiGym 3 (Chernyadev et al., 2024) is built upon MuJoCo (Todorov et al., 2012). We use Unitree H1 with two parallel grippers. We find that demonstrations available in the recent version of BiGym are not all successful. Therefore we adopt the strategy of replaying all the demonstrations and only use the successful ones as demonstrations. instead of discarding the failed demonstrations, we store them in a replay buffer as failure experiences. To avoid training with too few demonstrations, we exclude the tasks where the ratio of successful demonstrations is below 50%. Table 1 shows the list of 25 sparsely-rewarded mobile bi-manual manipulation tasks used in our experiments.

HumanoidBench HumanoidBench 4 (Sferrazza et al., 2024) is built upon MuJoCo (Todorov et al., 2012). We use Unitree H1 with two dexterous hands. We consider the first 8 locomotion tasks in the benchmark: Stand , Walk , Run , Reach , Hurdle , Crawl , Maze , Sit Simple . We use proprioceptive states and privileged task information instead of visual observations. Unlike BiGym and RLBench experiments, we do not utilize dueling network (Wang et al., 2016) and distributional critic (Bellemare et al., 2017) in HumanoidBench for faster experimentation.

RLBench RLBench 5 (James et al., 2020) is built upon CoppeliaSim (Rohmer et al., 2013) and PyRep (James et al., 2019). We use a 7-DoF Franka Panda robot arm and a parallel gripper. Following the setup of Seo et al. (2024), we increase the velocity and acceleration of the arm by 2 times. For all experiments, we use 100 demonstrations generated via motion-planning. Table 2 shows the list of 20 sparsely-rewarded visual manipulation tasks used in our experiments.

Hyperparameters We use the same set of hyperparameters across the tasks in each domain. For hyperparameters shared across CQN and CQNAS , we use the same hyperparameters for both algorithms for a fair comparison. We provide detailed hyperparameters for BiGym and RLBench experiments in Table 3 and HumanoidBench experiments in Table 4

Computing hardware For BiGym and Humanoid experiments, we use NVIDIA A5000 GPU with 24GB VRAM. With A5000, each BiGym experiment with 100K environment steps take 16 hours, and each HumanoidBench experiment with 10M environment steps take 40 hours. For RLBench experiments, we use NVIDIA RTX 2080Ti GPU, with which each experiment with 30K environment steps take 6.5 hours. We find that CQN-AS takes 13% more memory compared to CQN and is 40% slower than CQN. Overall, CQNAS is around 33% slower than running CQN because larger architecture slows down both training and inference.

Baseline implementation For CQN (Seo et al., 2024) and DrQ-v2+ (Yarats et al., 2022), we use the implementation available from the official CQN implementation 6 . For ACT (Zhao et al., 2023), we use the implementation from RoboBase repository 7 . For SAC (Haarnoja et al., 2018), DreamerV3 (Hafner et al., 2023), and TD-MPC2 (Hansen et al., 2024), we use results provided in HumanoidBench 8 repository (Sferrazza et al., 2024).

C Full description of CQN and CQN-AS

This section provides the formulation of CQN and CQNAS with n -dimensional actions.

C.1 Coarse-to-fine Q-Network

Let a l,n t be an action at level l and dimension n and a l t = { a l, 1 t , ..., a l,N t } be actions at level l with a 0 t being zero vector. We then define coarse-to-fine critic to consist of multiple Q-networks:

Where B denotes the number of bins. We optimize the critic network with the following objective:

where ¯ θ are delayed parameters for a target network (Polyak & Juditsky, 1992) and π l is a policy that outputs the action a l t at each level l via the inference steps with our critic, i.e., π l ( h t ) = a l t .

Action inference To output actions at time step t with the critic, CQN first initializes constants a n, low t and a n, high t with -1 and 1 for each n . Then the following steps are repeated for l ∈ { 1 , ..., L } :

Step 1 (Discretization): Discretize an interval [ a n, low t , a n, high t ] into B uniform intervals, and each of these intervals become an action space for Q l,n θ .

Step 2 (Bin selection): Find the bin with the highest Q-value, set a l,n t to the centroid of the selected bin, and aggregate actions from all dimensions to a l t .

Step 3 (Zoom-in): Set a n, low t and a n, high t to the minimum and maximum of the selected bin, which intuitively can be seen as zooming-into each bin.

We then use the last level's action a L t as the action at time step t .

Computing Q-values To compute Q-values for given actions a t , CQN first initializes constants a n, low t and a n, high t with -1 and 1 for each n . We then repeat the following steps for l ∈ { 1 , ..., L } :

Step 1 (Discretization): Discretize an interval [ a n, low t , a n, high t ] into B uniform intervals, and each of these intervals become an action space for Q l,n θ .

Step 2 (Bin selection): Find the bin that contains input action a t , compute a l,n t for the selected interval, and compute Q-values Q l,n θ ( h t , a l,n t , a l -1 t ) .

Step 3 (Zoom-in): Set a n, low t and a n, high t to the minimum and maximum of the selected bin, which intuitively can be seen as zooming-into each bin.

We then use a set of Q-values { Q l,n θ ( h t , a l,n t , a l -1 t ) } L l =1 for given actions a t .

C.2 Coarse-to-fine Critic with Action Sequence

Let a l t : t + K = { a l t , ..., a l t + K -1 } be an action sequence at level l and a 0 t : t + K be zero vector. Our critic network consists of multiple Q-networks for each level l , dimension n , and sequence step k :

We optimize the critic network with the following objective:

where π l K is an action sequence policy that outputs the action sequence a l t : t + K . In practice, we compute Q-values for all sequence step k ∈ { 1 , ..., K } and all action dimension n ∈ { 1 , ..., N } in parallel. This can be seen as extending the idea of Seyde et al. (2023), which learns decentralized Q-networks for action dimensions, into action sequence dimension. As we mentioned in Section 3.1, we find this simple scheme works well on challenging tasks with high-dimensional action spaces.

Architecture Let e k denote an one-hot encoding for k . For each level l , we construct features for each sequence step k as h l t,k = [ h t , a l -1 t + k -1 , e k ] . We encode each h l t,k with a shared MLP network and process them through GRU (Cho et al., 2014) to obtain s l t,k = f GRU θ ( f MLP θ ( h l t, 1 ) , ..., f MLP θ ( h l t,k )) . We use a shared projection layer to map each s l t,k into Q-values at each sequence step k , i.e., f proj θ ( s l t,k ) = { [ Q l,k θ ( h t , a l,n,b t + k -1 , a l -1 t : t + K )] B b =1 } N n =1 . We compute Q-values for all dimensions n ∈ { 1 , ..., N } at the same time with a big linear layer, which follows the design of Seo et al. (2024).

D Additional Preliminary Experiments

Offline RL with CQN-AS To further investigate whether CQN-AS formulation is compatible with offline RL, we conduct preliminary experiments on BiGym's Sandwich Remove task. Specifically, we combine CQN-AS with Cal-QL (Nakamoto et al., 2023) and train it on the dataset that consists of 26 successful demonstrations and 10 failed trajectories. We find that CQN-AS + Cal-QL achieves 33 ( ± 6.8) % while CQN + Cal-QL achieves 7 ( ± 14) %, which shows CQN-AS can be indeed effective for offline setup. We leave further exhaustive investigation as an interesting future work.

Experiments with ResNet-18 To investigate if using larger and stronger pre-trained vision encoders such as ResNet (He et al., 2016) can improve performance, we tried running CQN-AS with ResNet-18 encoder on BiGym tasks. However, we find that it requires a GPU with at least 48GB memory and is extremely slow to train. We will leave this direction of incorporating larger vision encoders in an efficient manner as a future direction.

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Justification: We have claimed that incorporating action sequences into reinforcement learning can be beneficial in the abstract and introduction, which clearly reflects the main contribution of this paper - CQNAS that uses action sequences in value learning. We have provided experimental results and analysis in the paper to support the claims in Figure 2, Figure 5, Figure 6, and Figure 8.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFollowing a recent trend in behavioral cloning for robot learning, the authors augment an existing reinforcement learning (RL) algorithm, Coarse-to-Fine Q-Network (CQN) with the capability to deal with action sequences. They evaluate their algorithm, CQN-AS, on 3 sets of benchmarks (BiGym, RLBench and HumanoidBench) and show that CQN-AS generally outperforms several RL baselines which do not play action sequences and behavioral cloning (BC) baselines that do. They also provide an ablation study.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper fills a gap: it seems that the benefits of using action sequences in RL has not been shown before\n- The paper is rather clear\n- The empirical study uses a large set of benchmarks\n- Results are generally good\n\nWeaknesses:\n- several points need further clarification (see below)\n- we could expect a larger effort on understanding WHY action sequences help. Despite ablations, the paper does not provide empirical clues on that. Maybe using much simpler environments coudl help.\n- the idea that the effect has to do with over-estimation bias is not supported (see below)\n- an empirical comparison to (Saanum et al. 2024) would improve the paper\n- the title is inadequate as there is no robot in this work\n\nQUESTIONS:\n*Major issues:*\n\n- In the introduction, the authors outline that actor-critic algorithms dealing with action sequences suffer from over-estimation bias. Then they advocate for critic-only algorithms with the argument that \"there is no separate actor that may exploit value functions\" and they pretend that as their contribution, CQN-AS, is based on a critic-only approach, it will avoid the value overestimation problem. First, the argument is wrong: DQN is a critic-only algorithm (the most basic one) and it suffers from over-estimation bias, as the mitigation with Double DQN clearly shows. Second, the authors do not measure over-estimation bias in CQN-AS in the empirical study, so they do not show that it mitigates over-estimation bias. A study of the role of over-estimation bias in RL algorithms dealing with action sequences could be a useful contribution of this paper...\n\n- About the N-step return, lines 112-117 the authors suggest that using from 1 to 3 steps works the best, but their empirical study only provides results with 1 (i.e. not N-step return) and 4 steps, which performs worse, hence suggesting that one should avoid using N-step return. This inconsistency must be solved, either by providing results with 2 and 3 steps, or by removing the N-step return idea from the main paper (showing that it fails in some Appendix may help readers avoid to try this again).\n\n- In Figures 5 and 6, the performance of all algorithms including CQN-AS always starts at 0, though the policy is pre-trained with a few (17 to 60) expert demonstrations. Do the figures start before training with the demonstrations? Otherwise, could the authors explain why?\n\n- Did all the baselines benefit from the same 17 to 60 expert demonstrations pre-training process? I assume it is the case for CQN, but for DrQ-v2+, it is less obvious. Otherwise I'm afraid the comparison is not scientifically valid.\n\n- I don't find the message from the comparison between *human-collected* and *synthetic (clean)* demonstrations (BiGym vs RLBench experiments) convincing enough. The authors do not provide compelling evidence that this feature is playing a key role, it could be other factors in the different benchmarks that explain the difference. A controlled experiment where using synthetic versus human-collected data is the only changing factor (e.g. same benchmark, just changing the dataset) would be necessary to evaluate the role of this feature. Besides, as it only plays a role in pre-training and then balanced sampling, it is unclear why it should make such a difference.\n\n- What are the differences between CQN-AS with the RL part ablated and BC-ACT? If they are close to be the same, I don't find Fig. 7b useful, as Fig. 5 already shows more or less the same results.\n\n*More local points:*\n\n- The authors could replace Figures 5 and 6 with performance profiles (see the Rliable library and \"Reinforcement learning at the edge of the statistical precipice\", Agrawal et al. NeurIPS 2021) and reject the full set of curves to an Appendix. The saved space could be used to include HumanoidBench results into the main paper\n- The authors seem to be using balanced sampling of expert demonstrations, they should give a ref to this approach (Ross&Bagnell 2012 or the more recent RLPD paper)\n- Did the authors try to ablate expert pre-training?\n- On the effect of action length, why didn't the authors try longer sequences (32, 64, etc.) as they found that the longer, the better? Using 50 is a common practice in Diffusion policy based BC...\n- The studies of the effect of temporal ensemble and of its magnitude (Figs. 7d and 7e) could be merged into a single study and the value of m is not given in Fig 7d.\n\n- In the conclusion: \"One limitation of our work is the lack of real-world robot evaluation,as RL with fragile humanoid hardware remains a significant challenge\" -> Well, this is not the right argument, as some robots are not fragile humanoids :)\n\nLIMITATIONS:\nThe authors do not discuss the computational aspects of CQN-AS wrt CQN: is it faster or slower to train? Faster or slower at inference time? Does it require a larger memory? All these aspects will be important when switching to real robots", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Coarse-to-fine Q-Network with Action Sequence (CQN-AS), a value-based RL method that explicitly predicts Q-values over sequences of actions rather than individual actions. The main claim is that modeling the return-to-go over action sequences leads to more accurate value estimation and better performance, especially in sparse-reward settings. This is supported by empirical results showing that CQN-AS often outperforms baseline methods on tasks from BiGym and RLBench.\n\nA key contribution is the formulation of the value function over sequences, which encourages the agent to reason more effectively about temporally extended behaviors. The authors support this claim through comparative results on several benchmarks and provide ablation studies showing that longer action sequences correlate with improved performance. However, while average performance improves, in some tasks gains are modest or inconsistent, which somewhat weakens the universality of the claim. A deeper analysis of when and why CQN-AS works best would help solidify its impact.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper presents a well-motivated problem and a clearly defined method. Experiments cover a diverse set of challenging sparse-reward tasks from BiGym and RLBench, supported by a reasonable set of ablations.\n\nThe paper is clearly written and well-structured. The presentation of ideas, contributions, and results is easy to follow. Ablation studies and visualizations help convey the impact of different design decisions effectively.\n\nThe idea of leveraging action sequences for value-based RL is timely and relevant, bridging insights from behavior cloning to reinforcement learning. However, in some cases/tasks, it is not clear that the proposed method significantly outperforms the baselines.\n\nThe method builds on existing ideas in sequence modeling and action prediction, applying them in a novel way to Q-value estimation. While the high-level motivation is not entirely new, the specific formulation and empirical exploration are original and interesting.\nThe method draws inspiration from recent trends in behavior cloning and sequence modeling (e.g., decision transformers), but applies these ideas in a novel way within a value-based RL framework—distinct from purely model-free or policy-gradient approaches. To the best of my knowledge, there is no prior value-based method that directly models Q-values over explicit action sequences as done here. This design is original and well-motivated, representing a meaningful contribution to the space of temporal abstraction and sequential reasoning in RL.\n\nSome experimental design choices are not well justified. For example, the statement that \"17 to 60 demonstrations\" were used leaves ambiguity: using 17 versus 60 demos can have a large impact on performance, particularly in sparse-reward settings. Clarifying this and providing more control over such variables would strengthen the experimental section.\n\nQUESTIONS:\nSome tasks show only marginal gains over baselines. Can the authors provide more detailed per-task results or analysis to clarify where CQN-AS is most effective?\n\nThe paper mentions using “17 to 60 demonstrations” per task. How is this number chosen? Performance can vary a lot wrt the number of demos used. Can you clarify the choice?\n\nThe ablation on sequence lengths shows significant performance improvements with longer action sequences. Is there an upper bound or threshold beyond which increasing the sequence length causes performance to degrade?\n\nHave the authors considered comparisons to sequence models?\n\nLIMITATIONS:\nThe authors are transparent about the method’s limitations on long-horizon tasks, where predicting the return of long action sequences becomes increasingly difficult. This is a meaningful constraint, especially for complex tasks requiring extended planning or multi-stage behaviors. While CQN-AS shows promise on mid-range horizons, its performance may degrade as the sequence length increases due to compounding uncertainty and training instability. This limits its scalability in more complex environments. It would be interesting to see future extensions that incorporate hierarchical sequence modeling or hybrid approaches that decouple high-level planning from low-level execution. Such strategies could help overcome the bottlenecks associated with long sequence modeling and extend the applicability of the method to broader domains.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Coarse-to-fine Q-Network with Action Sequence (CQN-AS), a novel reinforcement learning (RL) algorithm. The core idea is to enhance value learning in robotics by training a critic network to predict Q-values for sequences of actions rather than individual actions. This approach is motivated by observations that action sequences lead to lower validation loss in return-to-go prediction and that standard actor-critic methods struggle with value overestimation when using action sequences. Experiments show that CQN-AS successfully avoids this overestimation and demonstrates improved performance on various robotic simulation benchmark environments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n* The design of the CQN-AS is well-motivated by the observation that using action sequences lead to lower critic loss.\nExtensive evaluation on the performance of CQN-AS.\n* The evaluation is thorough.\n* Clear ablation of the individual algorithmic choices and their merits in (4.4).\n\nWeaknesses\n* Extension to use action sequences based on CQN looks like an incremental extension so I wouldn’t rate this work highly in terms of of novelty.\n\nQUESTIONS:\n* The author mentioned that CQN-AS may struggle to “achieve meaningful success rates on long-horizon tasks” (4.1). I think the paper would be made stronger if the authors include some potential experiments showing how some of the potential extensions can make CQN-AS perform even better.\n* Can you elaborate on on the baselines details of SAC and TD3 you used in obtaining figure 2b). I also don't entirely agree with your claim that action sequencing causes severe value over estimation from by looking at this plot - it looks from the plot that in fact TD3 and SAC may have even more value overestimation compared to the ones using action sequences.\n* In appendix table 3, there are mentions of hyperaparameters for C51. Do you use a distributional critic for both CQN and CQN-AS? If so, it would be nice to know how much a distributional critic helps with addressing value estimation. Given a distributional critic has bounded value, this means that you won't be able to see severe value overestimation as those reported in fig 2b.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Coarse-to-fine Q-Network with Action Sequence (CQN-AS), a novel value-based multi-step RL algorithm. CQN-AS trains a critic network to output Q-values over discretized action sequences in a coarse-to-fine manner. By generating multiple actions in parallel, it is less affected by compounding errors and makes value prediction easier. By completely removing the actor network, it is more robust to function approximation error. Experiments on multiple challenging benchmarks demonstrate that CQN-AS outperforms baseline algorithms in most tasks, particularly in sparse reward scenarios.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. The authors begin by analyzing two empirical observations: (1) the benefit of multistep actions for return-to-go prediction, and (2) the value overestimation in standard actor-critic algorithms when combined with multistep actions. These analyses effectively motivate their proposed method, resulting in a presentation that is logically coherent and clearly motivated.\n\n2. The writing is clear and the structure is well-organized throughout. Figure 2 effectively illustrates the core idea of the algorithm.\n\n3. The authors conduct extensive experiments to validate the effectiveness of the algorithm, along with comprehensive ablation studies that highlight the contribution of each component.\n\n### Weaknesses\nI agree with the authors' acknowledgment of the limitations and weaknesses of their method: (1) inferior performance on long-horizon tasks, (2) lack of real-world robot experiments, and (3) suboptimal results under torque control. However, the algorithm is well-motivated and innovative, and the experimental results strongly support the proposed motivation. I believe this work has the potential to inspire many future studies. In my opinion, it is worthy of acceptance and presentation at NeurIPS.\n\nQUESTIONS:\n1. Can CQN-AS be extended to the offline RL setting?\n\n2. Would replacing the GRU with a transformer network improve performance on long-horizon tasks?\n\n3. The paper mentions that removing the actor network prevents the actor from overexploiting the critic, making the algorithm more robust to function approximation errors in the critic. However, when performing Q-learning with discretized actions, the algorithm still selects the action that maximizes the next Q-value, which could also correspond to regions with large approximation errors. Why does this not lead to overestimation in such cases?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}