Publication
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The main advantage is th...
Publication
This paper proposes a simple linear Bayesian approach to reinforcement learning. We show that
with an appropriate basis, a Bayesian linear Gaussian model is sufficient for accurat...
AAAI
2012
12 years 3 months ago
2012
We present the first real-world benchmark for sequentiallyoptimal team formation, working within the framework of a class of online football prediction games known as Fantasy Foo...
CORR
2012
Springer
12 years 8 months ago
2012
Springer
Reinforcement learning addresses the dilemma between exploration to find profitable actions and exploitation to act according to the best observations already made. Bandit proble...
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Publication
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or ...
Publication
We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is relation among those tasks, then the information gained duri...
ICCCI
2011
Springer
13 years 10 days ago
2011
Springer
Multiagent reinforcement learning problems are especially difficult because of their dynamism and the size of joint state space. In this paper a new benchmark problem is proposed, ...
ATAL
2011
Springer
13 years 21 days ago
2011
Springer
A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-...
GECCO
2011
Springer
13 years 4 months ago
2011
Springer
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...