Near|optimal Reinforcement Learning in Factored MDPs

[1403.3741] Near-optimal Reinforcement Learning in Factored MDPs

Title:Near-optimal Reinforcement Learning in Factored MDPs ... Abstract:Any reinforcement learning algorithm that applies to all Markov decision ...

Near-optimal Reinforcement Learning in Factored MDPs

Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer (ФSAT) regret on some MDP, where T is.

Near-optimal reinforcement learning in factored MDPs - Volume 1

Abstract. Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer Ω(SAT) regret on some MDP, where T is the ...

Near-optimal Reinforcement Learning in Factored MDPs - arXiv

Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer Ω(√SAT) regret on some MDP, where T is.

Reinforcement Learning in Factored MDPs

We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms for ...

Near-Optimal Reinforcement Learning in Factored MDPs

Near-Optimal Reinforcement Learning in Factored MDPs. Ian Osband and Benjamin Van Roy. Stanford University. Page 2. • Measure: • Theorem: In a general MDP with ...

Efficient Reinforcement Learning in Factored MDPs - UPenn CIS

It achieves near-optimal performance in a running time and a number of actions that are polynomial in T and the number of parameters in the DBN-MDP, which in ...

[PDF] Near-optimal Reinforcement Learning in Factored MDPs

It is established that, if the system is known to be a factored MDP, it is possible to achieve regret that scales polynomially in the number of parameters ...

Reinforcement Learning in Factored MDPs: Oracle-Efficient ...

Assuming oracle access to an FMDP planner, they enjoy a Bayesian and a frequentist regret bound respectively, both of which reduce to the near-optimal bound eO( ...

Efficient reinforcement learning in factored MDPs - ACM Digital Library

We present a provably efficient and near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model can be ...

Near-Optimal Reinforcement Learning in Polynomial Time

ature has lacked algorithms for learning optimal behavior in general MDPs with provably ... Efficient reinforcement learning in factored MDPs. In Proceeding of ...

Efficient Reinforcement Learning in Factored MDPs with Application...

... optimal algorithms designed for non-factored MDPs, and improves on the best previous result for FMDPs~\citep{osband2014near} by a factor of ...

Near-optimal Regret Bounds for Reinforcement Learning in ...

Download Citation | Near-optimal Regret Bounds for Reinforcement Learning in Factored MDPs | Any learning algorithm over Markov decision processes (MDPs) ...

Model-Based Reinforcement Learning in Factored-State MDPs

Model-Based Reinforcement Learning in Factored-State MDPs ; INSPEC Accession Number: ; Persistent Link: https://xplorestaging.ieee.org/servlet/opac?punumber= ...

Towards Minimax Optimal Reinforcement Learning in Factored ...

The dynamics of the environment and the agent's interaction with it are typically modeled as a Markov decision process (MDP). We consider the specific setting ...

Efficient Reinforcement Learning in Factored MDPs with Application ...

Reinforcement learning (RL) in episodic, factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which leverages the ...

Polynomial Time Reinforcement Learning in Factored State MDPs ...

considered by Kearns and Koller (1999) assuming access to an efficient FMDP planner. More recently, Osband and Van Roy (2014) obtained near-optimal RL regret ...

EFFICIENT REINFORCEMENT LEARNING IN FACTORED MDPS ...

Near-optimal reinforcement learning in factored MDPs. In. Advances in Neural Information Processing Systems, pp. 604–612, 2014b. Daniel Russo and Benjamin ...

Efficient Structure Learning in Factored-State MDPs

Kearns, M. J., and Singh, S. P. 2002. Near-optimal reinforcement learning in polynomial time. Machine Learning 49(2–3):209–. 232. Strehl ...

Efficient Reinforcement Learning in Factored MDPs | Request PDF

... near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model... | Find, read and cite all the research you ...