ABC Reinforcement Learning
Paper in proceedings, 2013

We introduce a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The advantage is that we only require a prior distribution on a class of simulators. This is useful when a probabilistic model of the underlying process is too complex to formulate, but where detailed simulation models are available. ABC-RL allows the use of any Bayesian reinforcement learning technique in this case. It can be seen as an extension of simulation methods to both planning and inference. We experimentally demonstrate the potential of this approach in a comparison with LSPI. Finally, we introduce a theorem showing that ABC is sound.

reinforcement learning

approximate Bayesian computation


Christos Dimitrakakis

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Nikolaos Tziortziotis


Vol. 28 3 684-692

Areas of Advance

Information and Communication Technology

Subject Categories

Probability Theory and Statistics

Control Engineering

Computer Science

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