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AIIDE
2009
13 years 5 months ago
Examining Extended Dynamic Scripting in a Tactical Game Framework
Dynamic scripting is a reinforcement learning algorithm designed specifically to learn appropriate tactics for an agent in a modern computer game, such as Neverwinter Nights. This...
Jeremy Ludwig, Arthur Farley
NIPS
2003
13 years 6 months ago
Approximate Planning in POMDPs with Macro-Actions
Recent research has demonstrated that useful POMDP solutions do not require consideration of the entire belief space. We extend this idea with the notion of temporal abstraction. ...
Georgios Theocharous, Leslie Pack Kaelbling
IJCAI
2001
13 years 6 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
IJCAI
2007
13 years 6 months ago
Heuristic Selection of Actions in Multiagent Reinforcement Learning
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement Learni...
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna...
ATAL
2008
Springer
13 years 6 months ago
Autonomous transfer for reinforcement learning
Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typ...
Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
AAAI
2007
13 years 7 months ago
RETALIATE: Learning Winning Policies in First-Person Shooter Games
In this paper we present RETALIATE, an online reinforcement learning algorithm for developing winning policies in team firstperson shooter games. RETALIATE has three crucial chara...
Megan Smith, Stephen Lee-Urban, Hector Muño...
AGENTS
1999
Springer
13 years 9 months ago
General Principles of Learning-Based Multi-Agent Systems
We consider the problem of how to design large decentralized multiagent systems (MAS’s) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a...
David Wolpert, Kevin R. Wheeler, Kagan Tumer
AGENTS
2001
Springer
13 years 9 months ago
Using background knowledge to speed reinforcement learning in physical agents
This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program e...
Daniel G. Shapiro, Pat Langley, Ross D. Shachter
ICTAI
2003
IEEE
13 years 10 months ago
Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning
One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use al...
Marc Ricordeau
SBIA
2004
Springer
13 years 10 months ago
Heuristically Accelerated Q-Learning: A New Approach to Speed Up Reinforcement Learning
This work presents a new algorithm, called Heuristically Accelerated Q–Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algori...
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna...