On shallow planning under partial observability

Audrey Durand; Randy Lefebvre

RECORD

On shallow planning under partial observability

Randy Lefebvre, Audrey Durand

Artificial Intelligence · Submitted 22 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.

how it is stated · speculative 0.51 read the record →

Comment (0) Read on arXiv ↗

Cite ▾

Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,955 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.14 · human participants 0.03 · controlled comparison 0.10 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.09 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; show every role.

Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the bias-variance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent.	background 1.00	0.05	0.03	asserted 1.00
s1	This work investigates the impact of the discount factor on the bias-variance trade-off given structural parameters of the underlying Markov Decision Process.	contribution 1.00	0.72	0.05	unclear 0.50
s2	Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.	result 0.53	0.91	0.89	speculative 0.51

What kind of paper the classifier reports this is: empirical study 0.58 · evidence from simulation or computation 0.52.

What was examined, and what was not

sources read

arXiv listing abstract v2

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the bias-variance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 3 sentences without a confident label

Uses the method or tool of 1 paper

We consider the widely used PPO [ref] agent policy with the recommended hyperparameters for this task [ref] .

Proximal Policy Optimization Algorithms · Impact of Partial Observability on Deep RL · 1.00

Cites as support 1 paper

It could also be possible to develop automatic discount factor selection algorithms, as previous research papers have indicated that even naive form of this strategy can be very impactful [ref] .

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies · Conclusion · 0.96

Cites as background 5 papers

This complexity is due to many challenges such as sample efficiency of RL methods [ref] , risk/safety issues [ref] and partial observability [ref] .

A Review of Safe Reinforcement Learning: Methods, Theory and Applications · Introduction · 0.81
There is also plenty of work on properties of POMDPs such that approximations are more likely to yield good results [ref] .

When Is Partially Observable Reinforcement Learning Not Scary? · Related Work · 0.92
Different approximation schemes are explored through the literature like memoryless policies [ref] or compressions of histories [ref] .

The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs · Related Work · 0.97

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.