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Using plan-based reward shaping to learn strategies in StarCraft: Broodwar

https://doi.org/10.1109/cig.2013.6633622
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9780470316887openalex-snapshot
Markov Decision Processes
inferredcites →https://doi.org/10.1007/978-3-540-87536-9_37openalex-snapshot
Multigrid Reinforcement Learning with Reward Shaping
inferredcites →https://doi.org/10.1016/0004-3702(71)90010-5openalex-snapshot
Strips: A new approach to the application of theorem proving to problem solving
inferredcites →https://doi.org/10.1109/cig.2012.6374183openalex-snapshot
Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft:Broodwar
inferredcites →https://doi.org/10.1109/is.2008.4670492openalex-snapshot
Plan-based reward shaping for reinforcement learning
inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1142/s0219525911002998openalex-snapshot
AN EMPIRICAL STUDY OF POTENTIAL-BASED REWARD SHAPING AND ADVICE IN COMPLEX, MULTI-AGENT SYSTEMS
inferredcites →https://doi.org/10.1145/1273496.1273572openalex-snapshot
Automatic shaping and decomposition of reward functions
inferredcites →https://doi.org/10.1162/neco.1994.6.2.215openalex-snapshot
TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play
inferredcites →https://doi.org/10.1162/neco.2006.18.12.2936openalex-snapshot
Learning Tetris Using the Noisy Cross-Entropy Method
inferredcites →https://doi.org/10.1609/aiide.v8i3.12547openalex-snapshot
CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games
inferredcites →https://doi.org/10.2307/2291177openalex-snapshot
Markov Decision Processes: Discrete Stochastic Dynamic Programming.
inferredcites →https://doi.org/10.65109/jjtt8551openalex-snapshot
Dynamic potential-based reward shaping

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3804655openalex-extracted
Reward Shaping to Improve the Performance of Deep Reinforcement Learning in Inventory Management
inferred← citeshttps://doi.org/10.2139/ssrn.4622858openalex-extracted
Deep Reinforcement Learning for Capacity Allocation with Fare-Locking
inferred← citeshttps://doi.org/10.1002/aaai.12053openalex-snapshot
A survey of knowledge‐based sequential decision‐making under uncertainty
inferred← citeshttps://doi.org/10.1007/s00521-023-08211-xopenalex-snapshot
Graph learning-based generation of abstractions for reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.artint.2016.07.004openalex-snapshot
A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
inferred← citeshttps://doi.org/10.1016/j.ejor.2021.10.045openalex-snapshot
Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management
inferred← citeshttps://doi.org/10.1016/j.neucom.2017.02.096openalex-snapshot
Multi-objectivization and ensembles of shapings in reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.procs.2025.03.023openalex-snapshot
Reward Shaping for Improved Learning in Attacker-Defender Game
inferred← citeshttps://doi.org/10.1017/s026988891500017xopenalex-snapshot
Overcoming incorrect knowledge in plan-based reward shaping
inferred← citeshttps://doi.org/10.1017/s0269888918000279openalex-snapshot
Towards life-long adaptive agents: using metareasoning for combining knowledge-based planning with situated learning
inferred← citeshttps://doi.org/10.1088/2631-8695/adcde1openalex-snapshot
Reward shaping based D3QN algorithm for solving capacitated lot-sizing problem with stochastic demand
inferred← citeshttps://doi.org/10.1109/cig.2013.6633668openalex-snapshot
Front matter
inferred← citeshttps://doi.org/10.1109/ijcnn.2014.6889732openalex-snapshot
Multi-objectivization of reinforcement learning problems by reward shaping
inferred← citeshttps://doi.org/10.1155/2016/7090757openalex-snapshot
Enhancing Video Games Policy Based on Least-Squares Continuous Action Policy Iteration: Case Study on StarCraft Brood War and Glest RTS Games and the 8 Queens Board Game
inferred← citeshttps://doi.org/10.12794/metadc1505267openalex-snapshot
Proximal Policy Optimization in StarCraft
inferred← citeshttps://doi.org/10.1609/aaai.v35i12.17337openalex-snapshot
Learning Task-Distribution Reward Shaping with Meta-Learning
inferred← citeshttps://doi.org/10.1609/icaps.v31i1.16011openalex-snapshot
Guiding Robot Exploration in Reinforcement Learning via Automated Planning
inferred← citeshttps://doi.org/10.24963/ijcai.2019/324openalex-snapshot
Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards
inferred← citeshttps://doi.org/10.3390/app10134529openalex-snapshot
A Survey of Planning and Learning in Games
inferred← citeshttps://doi.org/10.3390/rs12040640openalex-snapshot
Robust Motion Control for UAV in Dynamic Uncertain Environments Using Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2107.06661openalex-snapshot
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
inferred← citeshttps://doi.org/10.5220/0010640200003063openalex-snapshot
Reward Prediction for Representation Learning and Reward Shaping
inferred← citeshttps://doi.org/10.5555/2615731.2616049openalex-snapshot
Knowledge revision for reinforcement learning with abstract MDPs
inferred← citeshttps://doi.org/10.5555/2772879.2772905openalex-snapshot
Policy Transfer using Reward Shaping
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