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Experimental Study on the Effect of Multi-Step Deep Reinforcement Learning in Pomdps
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-28929-8openalex-snapshot
A Concise Introduction to Decentralized POMDPs
inferredcites →https://doi.org/10.1007/s10458-012-9200-2openalex-snapshot
A survey of point-based POMDP solvers
inferredcites →https://doi.org/10.1007/s10994-021-05961-4openalex-snapshot
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50052-9openalex-snapshot
Learning policies for partially observable environments: Scaling up
inferredcites →https://doi.org/10.1016/j.eswa.2019.112963openalex-snapshot
Application of deep reinforcement learning to intrusion detection for supervised problems
inferredcites →https://doi.org/10.1016/j.robot.2007.01.004openalex-snapshot
Real-time hierarchical POMDPs for autonomous robot navigation
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1109/cisai54367.2021.00213openalex-snapshot
An Empirical Study on Bias Reduction: Clipped Double Q vs. Multi-Step Methods
inferredcites →https://doi.org/10.1109/robosoft48309.2020.9116003openalex-snapshot
Continuous Control of a Soft Continuum Arm using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1109/tro.2022.3188168openalex-snapshot
POMDP Planning Under Object Composition Uncertainty: Application to Robotic Manipulation
inferredcites →https://doi.org/10.1145/3424636.3426907openalex-snapshot
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning
inferredcites →https://doi.org/10.1177/0278364920987859openalex-snapshot
How to train your robot with deep reinforcement learning: lessons we have learned
inferredcites →https://doi.org/10.1609/aaai.v30i1.10295openalex-snapshot
Deep Reinforcement Learning with Double Q-Learning
inferredcites →https://doi.org/10.1609/aaai.v32i1.11631openalex-snapshot
Multi-Step Reinforcement Learning: A Unifying Algorithm
inferredcites →https://doi.org/10.1609/aaai.v32i1.11796openalex-snapshot
Rainbow: Combining Improvements in Deep Reinforcement Learning
inferredcites →https://doi.org/10.3390/app10114011openalex-snapshot
Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data
inferredcites →https://doi.org/10.48550/arxiv.1507.06527openalex-snapshot
Deep Recurrent Q-Learning for Partially Observable MDPs
inferredcites →https://doi.org/10.48550/arxiv.1511.06581openalex-snapshot
Dueling Network Architectures for Deep Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1604.06778openalex-snapshot
Benchmarking Deep Reinforcement Learning for Continuous Control
inferredcites →https://doi.org/10.48550/arxiv.1801.01290openalex-snapshot
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
inferredcites →https://doi.org/10.48550/arxiv.1802.09477openalex-snapshot
Addressing Function Approximation Error in Actor-Critic Methods
inferredcites →https://doi.org/10.48550/arxiv.1809.07731openalex-snapshot
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
inferredcites →https://doi.org/10.48550/arxiv.2006.07442openalex-snapshot
Self-Imitation Learning via Generalized Lower Bound Q-learning
inferredcites →https://doi.org/10.48550/arxiv.2006.12692openalex-snapshot
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.2010.08843openalex-snapshot
Approximate information state for approximate planning and reinforcement learning in partially observed systems
inferredcites →https://doi.org/10.48550/arxiv.2105.11674openalex-snapshot
Unbiased Asymmetric Reinforcement Learning under Partial Observability
inferredcites →https://doi.org/10.48550/arxiv.2107.08577openalex-snapshot
Structured World Belief for Reinforcement Learning in POMDP
inferredcites →https://doi.org/10.48550/arxiv.2107.14171openalex-snapshot
Tianshou: a Highly Modularized Deep Reinforcement Learning Library
inferredcites →https://doi.org/10.48550/arxiv.2108.03298openalex-snapshot
Operator-Composition Variance in robomimic Multi-Human Benchmarks: preregistration, code, and results
inferredcites →https://doi.org/10.48550/arxiv.2303.01859openalex-snapshot
POPGym: Benchmarking Partially Observable Reinforcement Learning
What points at this work
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inferred← citeshttps://doi.org/10.1016/j.aei.2026.105356openalex-snapshot
Uncertainty-aware and generalizable task prediction for human–robot collaboration in on-site timber assembly
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