Empowering LLM Agents with Zero-Shot Optimal Decision-Making through Q-learning

Authors: Jiajun Chai, Sicheng Li, Yuqian Fu, Dongbin Zhao, Yuanheng Zhu
Primary area: applications to robotics, autonomy, planning
Keywords: Large language models, Agent, Optimal decision-making

Abstract:
Large language models (LLMs) are trained on extensive text data to gain general comprehension capability. Current LLM agents leverage this ability to make zero- or few-shot decisions without reinforcement learning (RL) but fail in making optimal decisions, as LLMs inherently perform next-token prediction rather than maximizing rewards. In contrast, agents trained via RL could make optimal decisions but require extensive environmental interaction. In this work, we develop an algorithm that combines the zero-shot capabilities of LLMs with the optimal decision-making of RL, referred to as the Model-based LLM Agent with Q-Learning (MLAQ). MLAQ employs Q-learning to derive optimal policies from transitions within memory. However, unlike RL agents that collect data from environmental interactions, MLAQ constructs an imagination space fully based on LLM to perform imaginary interactions for deriving zero-shot policies. Our proposed UCB variant generates high-quality imaginary data through interactions with the LLM-based world model, balancing exploration and exploitation while ensuring a sub-linear regret bound. Additionally, MLAQ incorporates a mixed-examination mechanism to filter out incorrect data. We evaluate MLAQ in benchmarks that present significant challenges for existing LLM agents. Results show that MLAQ achieves a optimal rate of over 90\% in tasks where other methods struggle to succeed. Additional experiments are conducted to reach the conclusion that introducing model-based RL into LLM agents shows significant potential to improve optimal decision-making ability. Our interactive website is available at http://mlaq.site.

Decision: accept (poster)
Reviewer scores: 8, 5, 8, 3, 3

Reviews and discussion:
<thread>
creation date:
1730494256004

thread_type:
official_review

<thread_object>
creator: reviewer_zk4e

<content>
summary:
In this work, the authors develop an algorithm that combines the zero-shot capabilities of large language models (LLMs) with the decision-making strengths of reinforcement learning (RL). They introduce the Model-based LLM Agent with Q-Learning (MLAQ), which leverages Q-learning to derive optimal policies from transitions stored in memory. Unlike traditional RL agents that rely on data collected from direct environmental interactions, MLAQ constructs an "imagination space" based entirely on the LLM to simulate interactions and derive zero-shot policies. The results indicate that MLAQ achieves an optimal success rate of over 90% on tasks where other methods have struggled to perform effectively.

strengths:
- The innovation of the initial idea is good but the works has a lot of engineering! The method needs to be reformed.

weaknesses:
-  The abstraction and introduction is well written but the rest is not fluent. 
- LLM-based framework for an agent is the same as to decision transformers.  The author did not talk about that and even compare with this 
- The paper is not well-organized! Some terms need to be defined before going to the details of the method. 
- Some definitions are not clear: like what is exactly imagination space. 
- They said the work does not use environmental tools but they use T which is all information about the environment including actions, states, ...
- There are other type of works which are zero-shot and they paper did not mention them in either related work or introduction: Sub-goal Distillation: A Method to Improve Small Language Agents.

questions:
- What does this mean "the ground-truth available actions δ(s)"? Node selection is not clear. 
- The paper claimed for a zeros-shot approach! It is true but at the same time there is a lot of engineering part in the method like correct the imaginary transitions to refine the LLM-based world model, which makes the concept of zero-shot unsatisfied even though it is zero-shot!
- The criteria for the results are not enough! That should be proper comparison with other methods.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730691883386

thread_type:
official_review

<thread_object>
creator: reviewer_xlz6

<content>
summary:
This paper considers the problem of enabling LLM agents to complete long-horizon tasks. To accomplish this, the paper proposes a method that combines Q-learning, a way to generate imaginary data using LLMs, an MCTS-style planner that acts in that space, a mechanism to improve the quality of the imaginary data, and a variant of UCB that facilitates exploration.

strengths:
- Addresses an interesting problem of enabling LLMs to solve long-horizon tasks
- Strong performance on prior benchmarks
- Comparisons to relevant prior works
- Ablations confirm the importance of various components in BlocksWorld

weaknesses:
Overall, the clarity of the writing prevents me from recommending this paper for acceptance. I tried reading parts of the paper several times, but I unfortunately still don't understand truly how the method works. The writing seems both hard to follow/understand and missing important information. (I have published papers in the realm of LLMs, LLM agents, Q-learning/reinforcement learning, and model-based RL, so I should have sufficient background to understand the submission.)

It's hard to go through every part of the paper and describe what is hard to follow or missing, but here are a few examples:
- In the intro: It’s not clear what problem statement this paper is aiming to address. The sentences do not have a clear train-of-thought, and concepts are introduced without fully explaining them / without giving concrete examples.
- Important details are missing: how is the Q-function represented? The preliminaries describe a tabular representation, but this doesn’t seem applicable to the language setting. Is there a policy improvement step using the Q-function? If so, how is it done? How do each of the components interact? What concretely is the action space being considered? What is the breadth of the actions that the LLM can take?
- The term “environmental tools” is used extensively, but it’s not entirely clear what it means. It hasn't been used in prior papers to my knowledge, and isn't described clearly in this paper. It reminds me of LLM "tools", but based on the brief description in the intro, this seems incorrect and it seems to more be referring to assumptions that the method makes.
- The paper is not self-contained, and assumes detailed knowledge of specific prior works, e.g. Mandi et al to understand the RoCo task.
- The figures are helpful, but quite abstract
- The abstract refers to a website (https://mlaq.site/) that is empty
- Qualitative examples of the agent performance would be helpful
- Which LLM is used in the experiments? How large is it?


Secondarily, the method is very complex involving many different components. This fact combined with the lack of clarity in the paper means that it would be impossible to implement the method based on the description in the paper. I appreciate the ablation study though.

questions:
see weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1730759384099

thread_type:
official_review

<thread_object>
creator: reviewer_fsc6

<content>
summary:
In this paper, the authors present a Model-based LLM Agent with Q-learning (MLAQ), which employs Q-learning to derive optimal policies from transitions within memory. Unlike classic RL agents that collect data via environment interactions, MLAG leverages LLM to perform imaginary interactions where LLM can be served or viewed as a world model for the environment simulation. This allows the policies to learn to optimize under the imaginary space. Further, the authors employ a mixed-examination mechanism that utilizes environmental interaction and LLM-based self-examine to enhance the quality of imaginary success. Empirical experiments show that the method can significantly improve the LLM agent performance on several benchmarks, showing promising results.

strengths:
- The idea of using LLM to simulate world models is novel, and the authors give a detailed description of how to leverage LLMs to construct the model to simulate the environment;
- The verification part can guarantee that the model is more accurate than naively simulating the trajectories;

- Empirical experiments show that the idea can achieve significantly better performance, especially for long trajectories;

weaknesses:
- The idea is only evaluated on a very toy game domain. It would be good to see and discuss whether the method can be extended to more complex agent benchmarks, such as function calling, tau-bench, etc.

questions:
- Please see my questions in the weakness session
</content>
</thread_object>

</thread>
<thread>
creation date:
1730849349678

thread_type:
official_review

<thread_object>
creator: reviewer_4yyk

<content>
summary:
The paper presents a novel approach for integrating Q-learning with Large Language Models (LLMs) to enable zero-shot optimal decision-making. The authors propose a MLAQ framework, which includes Q-planner, a memory module, and an imagination space. The model leverage LLM as world model for agent to optimize its decision-making using Q-learning without relying on environmental interactions. The framwork also recruits a Mixed-Examination mechanism for transition verification. Finally, the experiments results outperforms existing methods in optimal decision-making tasks.

strengths:
1. The paper claims an important point of "making optimal decisions" and is well-written. 

2. The authors claim that this model is the first work to perform complete RL-based optimization process based on MDP scenario. The idea of generating imaginary transitions with LLMs for Q-learning optimize the policy makes sense to me. 

3. The experimental results are good in both tasks.

weaknesses:
1. My biggest concern is about the cost of this framework. While the paper claims that the MLAQ framework can operate without direct interaction with environmental tools by leveraging an LLM-based imagination space, this approach may still incur significant computational and financial costs. LLM interactions, especially those involving step-by-step task simulations, are resource-intensive in terms of both token usage and processing time. Additionally, in ablation experiments, tasks with 8 steps in BlocksWorld were shown to consume at least 40k tokens, which may be expensive for scaling up to complex tasks or multi-agent scenarios. The reliance on LLMs for generating and validating transitions could offset the savings from not interacting with real environments, especially if these transitions require multiple iterations for accuracy.

2. Although MLAQ aims to minimize reliance on real-world data, it still requires occasional feedback from the environment to correct inaccuracies in LLM-generated transitions. In Algorithm 1, it seems the stepwise reward is still from the environment. Then, how does the model deal with sparse or delayed reward situation, e.g. in Figure 3 you can only get binary reward at the last step? In this way, the LLM world model is only used for simulate success trajectories or also provide reward? 

3. The LLM-based world model is designed to act as a proxy for real environmental interactions, but this approach may have inherent limitations, especially for tasks requiring high-fidelity simulations or complex physical dynamics. To be more specific, LLMs understanding of actions and outcomes might not always align with real-world behaviors. Does author have more analysis on this?

questions:
1. Since the LLM here serves as a model-based simulator of the real environment and the policy is generated by the Q-function, is it appropreiate to call this "LLM agent"? The action or policy is not diorectly generated by LLM, could this framework be more accurately described as a Q-learning agent that uses an LLM-based world model?

2. I'm still unclear about what the training data is for Q-planner. Is it trained on the synthetic step-wise reward or the ground truth trajectories which is from the interaction with environment?
</content>
</thread_object>

</thread>
<thread>
creation date:
1731108469682

thread_type:
official_review

<thread_object>
creator: reviewer_ouuc

<content>
summary:
This paper proposes the Model-based LLM Agent with Q-Learning (MLAQ) to address the gap in optimal decision-making capabilities for large language model (LLM) agents. By combining Q-learning with LLMs, MLAQ achieves zero-shot optimal decision-making by using an LLM-generated imagination space, enabling the agent to derive policies without direct environmental interaction. Notably, the authors introduce a UCB variant to balance exploration and exploitation, and a mixed-examination mechanism to enhance the quality of imaginary data. The framework shows superior performance across single-agent and multi-agent benchmarks (BlocksWorld and RoCo-benchmark), surpassing existing methods in achieving optimal decisions.

strengths:
1. MLAQ presents a novel integration of Q-learning and LLMs, using an LLM-based imagination space to enable optimal decision-making without direct environmental data. This imaginative interaction is a valuable innovation, given the limitations of previous approaches relying heavily on environmental feedback.

2 The UCB variant and mixed-examination mechanisms are technically sound and provide meaningful contributions. These components contribute to reducing computational requirements and minimizing regret, which are crucial in complex decision-making scenarios.

3 The empirical results, showing over 90% optimal success in challenging benchmarks, convincingly demonstrate MLAQ's ability to outperform existing LLM agents. The performance on long-horizon tasks suggests MLAQ’s potential for real-world applications requiring complex decision-making sequences.

4 The proposed MLAQ framework appears modular and adaptable, making it feasible for extension to various domains and applications, including future deployment on physical robotic platforms.

weaknesses:
1. While the technical elements (such as the UCB variant) are well-motivated, the theoretical analysis could be expanded, especially concerning the convergence properties of Q-learning within the LLM-based imagination space. Further proofs would add rigor to the framework's guarantees.

2. The mixed-examination mechanism, while useful, places a considerable dependency on LLM self-correction capabilities. Although current models like GPT-4 demonstrate adequate self-examination, reliance on this might limit robustness, especially if the model is deployed in new or noisy environments where the LLM might hallucinate.

3. The method has been evaluated on BlocksWorld and RoCo-benchmark, which are well-structured tasks. Further evaluations in more unstructured environments could provide insight into the adaptability of MLAQ in real-world scenarios with higher variability.

questions:
1. Can you provide more theoretical justification or proofs for the convergence properties of Q-learning within the LLM-based imagination space? Specifically, under what conditions does MLAQ guarantee convergence to an optimal policy?

2. Given that MLAQ relies heavily on the LLM's self-examination for correcting imaginary transitions, how robust is the framework in handling LLM hallucinations? Have you explored scenarios where the LLM produces incorrect or inconsistent outputs, and if so, how does MLAQ mitigate this risk?

3. Could you discuss how sensitive MLAQ’s performance is to the hyperparameters used in the UCB variant (e.g., coefficients and confidence bounds)? Understanding this could help gauge the stability and generality of the method across different domains.

4. The MLAQ framework appears computationally intensive due to iterative Q-learning and the replay buffer management. Could you elaborate on the computational demands of MLAQ relative to traditional LLM agents? Are there strategies within the framework to reduce this overhead?

5. Have you tested MLAQ in more open-ended or unstructured environments, such as robotic tasks with real-world constraints (e.g., noisy sensors, dynamic obstacles)? If not, do you foresee any limitations or necessary modifications to make the model applicable to such environments?

6. Could you share insights on the types of tasks or domains where MLAQ may struggle to achieve optimal decision-making? Understanding these limitations can help contextualize the framework’s applicability.

7. Do you see potential for enhancing the LLM-based self-examination process, perhaps by combining it with non-LLM-based checks? For instance, could integrating environmental feedback more dynamically (e.g., through real-time adaptive learning) reduce reliance on the LLM’s introspective accuracy?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732092749445

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Overall response for all reviewers

comment:
Firstly, we extend our gratitude to **ALL** reviewers for their time and effort in reviewing this manuscript. We are particularly grateful for their positive feedback, as three reviewers acknowledged that the paper is **well-written and provides a comprehensive description of the methodology**, and most reviewers acknowledged the valuable innovations presented in this work and recognized our thorough experimental validation of **MLAQ's zero- and few-shot optimal decision-making capabilities**. We have addressed the reviewers' concerns by supplementing more descriptions as suggested, which we hope will help better understand MLAQ's contributions. We look forward to a positive re-evaluation based on these revisions.

During the rebuttal period, we provide **two new information** about MLAQ for the reviewers: 
- Drawing from MBPO's theoretical framework, we provide a **theoretical analysis of the converged policy** when LLM-based world models contain errors
- We evaluate MLAQ on the **much more complex and open-ended Crafter environment**, comparing it with existing LLM agents and RL agents. Results indicate that while MLAQ can be effectively applied to stochastic, partially observable environments, it is more suitable for deterministic, fully observable settings (**see "Experiments on Crafter" for more detail**).

Additionally, both the GitHub repository (https://github.com/laq2024/MLAQ/) and interactive demonstration website (http://mlaq.site) are completely anonymized, created through newly registered accounts specifically for this conference. We affirm that no personal information has been included in either link.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732092875534

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Experiments on Crafter

comment:
Many reviewers mentioned more experimental results of MLAQ in complex scenarios. We first categorize scenarios into two types to evaluate their complexity: 
+ **Deterministic & fully observable**. RoCo and BlocksWorld belong to this category, where the state information for LLM is fully-known, and executing an action can only lead to one deterministic next state (when moving a cube from A to B, the cube can only appear at B rather than at another random place C). This type of scenario can cover most practical applications.
+ **Stochastic & partially observable (Much more complex)**. This category involves partially observable settings in open-ended environments, such as in Minecraft, where the overall map information is unknown, and player exploration on the map is partially observable (for example, when digging downward, players don't know whether the next cube is stone or coal).

To validate MLAQ's performance in partially observable scenarios with high complexity, we chose **Crafter[1] (a 2D version of MineCraft)** as our experimental environment. 
+ In Crafter, players need to mine diamonds on a 64*64 map, and there are 16 sub-goals to complete (collecting woods, stones, make wood pickaxe, etc.), making it **a challenging task with extremely long decision sequences**. Additionally, the reward signals are only provided when successfully achieving a sub-goal.
+ Players can only observe a 9 * 9 local map, and we adopt SmartPlay's natural language observation setting: for multiple items in the view, only the nearest one will be shown in the **observation**. For example, even if there are 3 trees in the view, the observation will only show "tree is 3 steps to your north-east", which further intensifies the partial observability of Crafter.

Since the same state-action pair may lead to different next states for **stochastic domains**, we made two minor modifications to MLAQ:
+ We remove the Prediction Checker, as it's difficult to provide effective verification in the cases with stochasticity.
+ We remove MLAQ's re-utilization of memory transitions during planning, because direct re-utilization would lose stochasticity for **potential multiple next states**. However, this can be adjusted as needed in practical, such as starting random re-utilization when the number of next states exceeds a threshold.

**Here are the experimental results**:

| Methods | Human[1] | MLAQ-gt | MLAQ-script | MLAQ | DiVE[3] | EnvGen[4] | Dreamer V3[5] | PPO[6] | Random [1] |
| - | - | - | - | - | - | - | - | - | - |
| Scores | 50.5 | 46.2 | 42.6 | 39.9 | 35.9 | 32.2 | 14.5 | 4.6 | 1.6 |

We calculate the scores using the same methodology as existing works, which is determined by the ratio of unblocked achievements. The data of MLAQ are obtained using the same testing methods as SmartPlay[2], while other data are sourced from their original papers or from [1]. In Crafter, **the move action exhibits strong stochasticity**, making it impossible for players to infer the next observation.

Therefore, we conduct three experiments where the only difference lies in how the world model predict next observations **for move actions**, while all other modules **remain consistent** with the original MLAQ.
+ **MLAQ-gt** utilizes ground-truth environment simulation for move actions, providing the exact next observation (next state) for given observation-action (state-action) pair, thus effectively **eliminating movement stochasticity**.
+ **MLAQ-script** generates next observations for move actions through **scripted randomization** (e.g., after moving north, nine unknown areas appear at the northernmost position, with randomly assigned items like stone, sand).
+ **MLAQ** reconstructs the aforementioned script into prompts, allowing LLM to fully implement an **LLM-based world model**.

The results align with expectations: MLAQ-gt achieves best performance using ground-truth environment for move actions, consistently crafting iron pickaxes but struggling to explore the location of diamonds within limited steps in 64 * 64 maps. MLAQ-script and MLAQ **outperform other existing LLM agents and RL agents**, but significantly below MLAQ-gt, indicating stochasticity substantially impacts MLAQ. These experimental data are averaged from eight tests, and we commit to supplementing more comprehensive data in the camera-ready version and incorporating it into the revised manuscript.

[1] Hafner, Danijar. "Benchmarking the Spectrum of Agent Capabilities." ICLR, 2022.

[2] Wu, Yue, et al. "SmartPlay: A Benchmark for LLMs as Intelligent Agents." ICLR, 2024.

[3] Sun, Zhiyuan, et al. "Enhancing Agent Learning through World Dynamics Modeling." EMNLP Findings, 2024.

[4] Zala Abhay, et al. "EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents." COLM, 2024.

[5] Hafner, Danijar, et al. "Mastering diverse domains through world models." arXiv, 2023.

[6] Schulman, John, et al. "Proximal policy optimization algorithms." arXiv, 2017.
</content>
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</thread>


Metareview:
metareview:
The core contribution of this work lies in leveraging LLMs as a world model to generate imaginary transitions for Q-learning. MLAQ includes a UCB-inspired planner and a mixed-examination mechanism to balance exploration, enhance transition quality, and achieve sub-linear regret bounds. Experiments demonstrate MLAQ's efficacy in benchmarks like BlocksWorld and Crafter.

The idea of using an LLM as a world model/simulator is forward-looking. Despite being an obvious idea, little prior work has shown effectiveness in this approach, so the authors' contribution is valuable. In a long run, LLM-based simulation eliminates the need for extensive environmental interactions. Empirical results show strong performance, particularly on long-horizon decision-making benchmarks.

The biggest criticism lies in writing clarity. Some reviewers found the paper difficult to follow, citing unclear problem statements, undefined terms, and missing implementation details. "Empowering LLM agents with zero- or few-shot optimal decision-making capabilities" is too generic of a problem statement. Concerns were also raised about the validity of the zero-shot claim due to the mixed-examination mechanism requiring some environmental feedback.

The authors are expected to revise the paper and improve the writing clarity significantly in the final submission according to reviewers feedback. It will be interesting to see the effectiveness of the proposed approach in more complex and practical benchmarks.