231 search results - page 33 / 47 » Active Learning in Partially Observable Markov Decision Proc... |
136
Voted
NECO
2007
15 years 3 months ago
2007
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
158
Voted
RSS
2007
15 years 5 months ago
2007
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
126
Voted
JAIR
2008
15 years 3 months ago
2008
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
131
Voted
NN
2010
Springer
15 years 2 months ago
2010
Springer
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
131
Voted
ATAL
2007
Springer
15 years 10 months ago
2007
Springer
A problem of planning for cooperative teams under uncertainty is a crucial one in multiagent systems. Decentralized partially observable Markov decision processes (DECPOMDPs) prov...