Efficient reinforcement learning in factored MDPs

Efficient Reinforcement Learning in Factored MDPs - UPenn CIS

Efficient Reinforcement Learning in Factored MDPs. Michael Kearns. AT&T Labs [email protected]. Daphne Koller. Stanford University [email protected].

Efficient Reinforcement Learning in Factored MDPs with Application ...

We study a new formulation of constrained RL, known as RL with knapsack constraints (RLwK), and provides the first sample-efficient algorithm based on FMDP-BF.

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, ...

Reinforcement Learning in Factored MDPs

Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting · Authors · Abstract · Name Change ...

Efficient reinforcement learning in factored MDPs - ACM Digital Library

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

Efficient Reinforcement Learning in Factored MDPs - CiteSeerX

In particular, it cannot be ap- plied to MDPs in which the transition probabilities are rep- resented in the factored form of a dynamic Bayesian network. (DBN).

EFFICIENT REINFORCEMENT LEARNING IN FACTORED MDPS ...

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

Efficient Structure Learning in Factored-State MDPs

Our method learns the DBN structures as part of the reinforcement-learning process and provably provides an efficient learning algorithm when combined with fac-.

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

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

Efficient Reinforcement Learning in Factored MDPs with Application ...

May 3rd, 2021. Xiaoyu Chen (Peking University). Factored MDPs. May 3rd, 2021. 1/7. Page 2. Tabular Episodic MDP. For tabular MDPs, the regret bounds ...

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

In this paper, we provide the first algorithm that learns the structure of the FMDP while minimizing the regret.

Near-optimal Reinforcement Learning in Factored MDPs

The vast majority of efficient reinforcement learning has focused upon the tabula rasa setting, where little prior knowledge is available about the environment ...

[PDF] Efficient Reinforcement Learning in Factored MDPs

This work presents a provably efficient and near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model can ...

Review for NeurIPS paper: Reinforcement Learning in Factored MDPs

Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting. Meta Review. After discussing ...

[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 ...

Efficient Reinforcement Learning in Factored MDPs | Request PDF

Request PDF | Efficient Reinforcement Learning in Factored MDPs | We present a provably efficient and near-optimal algorithm for reinforcement learning in ...

Efficient Reinforcement Learning in Factored MDPs

Recommendations · Near-optimal reinforcement learning in factored MDPs. NIPS'14: Proceedings of the 27th International Conference on Neural Information ...

Sample Efficient Learning with Feature Selection for Factored MDPs

In reinforcement learning, state is often represented by feature vectors. Prior sample complexity bounds scale with the complexity of all features.

[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 ...

borea17/efficient_rl: Reimplementation of "An Object ... - GitHub

Factored MDPs enable an effective parametrization of transition and reward dynamics by using dynamic Bayesian networks (DBNs) to represent partial dependency ...