215 search results - page 5 / 43 » Model-Based Reinforcement Learning with Continuous States an... |
107
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ICML
2006
IEEE
16 years 2 months ago
2006
IEEE
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
125
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AROBOTS
1999
15 years 1 months ago
1999
We use reinforcement learning (RL) to compute strategies for multiagent soccer teams. RL may pro t signi cantly from world models (WMs) estimating state transition probabilities an...
115
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ICANN
2010
Springer
15 years 2 months ago
2010
Springer
We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuou...
ICML
2003
IEEE
16 years 2 months ago
2003
IEEE
Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning...
123
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ICML
1996
IEEE
15 years 5 months ago
1996
IEEE
This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcement Learning, in the continuous case, i.e. continuous state-space and time. The eval...