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Found 9 papers, 3 papers with code
Improved Analysis of UCRL2 with Empirical Bernstein Inequality
We consider the problem of exploration-exploitation in communicating Markov Decision Processes.
Concentration Inequalities for Multinoulli Random Variables
We investigate concentration inequalities for Dirichlet and Multinomial random variables.
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
The exploration bonus is an effective approach to manage the exploration-exploitation trade-off in Markov Decision Processes (MDPs).
Regret Bounds for Learning State Representations in Reinforcement Learning
We consider the problem of online reinforcement learning when several state representations (mapping histories to a discrete state space) are available to the learning agent.
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
We introduce and analyse two algorithms for exploration-exploitation in discrete and continuous Markov Decision Processes (MDPs) based on exploration bonuses.
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
While designing the state space of an MDP, it is common to include states that are transient or not reachable by any policy (e. g., in mountain car, the product space of speed and position contains configurations that are not physically reachable).
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
We introduce SCAL, an algorithm designed to perform efficient exploration-exploitation in any unknown weakly-communicating Markov decision process (MDP) for which an upper bound $c$ on the span of the optimal bias function is known.
Regret Minimization in MDPs with Options without Prior Knowledge
The option framework integrates temporal abstraction into the reinforcement learning model through the introduction of macro-actions (i. e., options).
Exploration--Exploitation in MDPs with Options
While a large body of empirical results show that temporally-extended actions and options may significantly affect the learning performance of an agent, the theoretical understanding of how and when options can be beneficial in online reinforcement learning is relatively limited.
