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Found 10 papers, 3 papers with code
Adaptive Multi-Goal Exploration
We introduce a generic strategy for provably efficient multi-goal exploration.
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning
In this paper, we study the problem of representation learning and exploration in reinforcement learning.
UCB Momentum Q-learning: Correcting the bias without forgetting
We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new algorithm for reinforcement learning in tabular and possibly stage-dependent, episodic Markov decision process.
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited
In this paper, we propose new problem-independent lower bounds on the sample complexity and regret in episodic MDPs, with a particular focus on the non-stationary case in which the transition kernel is allowed to change in each stage of the episode.
Fast active learning for pure exploration in reinforcement learning
Realistic environments often provide agents with very limited feedback.
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric.
Adaptive Reward-Free Exploration
Reward-free exploration is a reinforcement learning setting studied by Jin et al. (2020), who address it by running several algorithms with regret guarantees in parallel.
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for planning in a Markov Decision Process in which transitions have a finite support.
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric.
Planning in entropy-regularized Markov decision processes and games
We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the SmoothCruiser.
