181 search results - page 11 / 37 » State Space Reduction For Hierarchical Reinforcement Learnin... |
ECML
2006
Springer
13 years 11 months ago
2006
Springer
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
AAAI
1993
13 years 8 months ago
1993
This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis function...
AGENTS
1999
Springer
13 years 11 months ago
1999
Springer
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
ATAL
2010
Springer
13 years 8 months ago
2010
Springer
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...