43 citations · 50 across the 8 of their papers we have counts for
10 papers
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning
Yiran Wang, Chenshu Liu, Yunfan Li +3
The exploration \& exploitation dilemma poses significant challenges in reinforcement learning (RL). Recently, curiosity-based exploration methods achieved great success in tacklin…
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing
Sanae Amani, Khushbu Pahwa, Vladimir Braverman +1
Recently, DARPA launched the ShELL program, which aims to explore how experience sharing can benefit distributed lifelong learning agents in adapting to new challenges. In this pap…
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
Sanae Amani, Lin F. Yang, Ching-An Cheng
We study lifelong reinforcement learning (RL) in a regret minimization setting of linear contextual Markov decision process (MDP), where the agent needs to learn a multi-task polic…
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
Sanae Amani, Tor Lattimore, András György +1
We study distributed contextual linear bandits with stochastic contexts, where agents act cooperatively to solve a linear bandit-optimization problem with -dimensional featu…
Safe Reinforcement Learning with Linear Function Approximation
Sanae Amani, Christos Thrampoulidis, Lin F. Yang
Safety in reinforcement learning has become increasingly important in recent years. Yet, existing solutions either fail to strictly avoid choosing unsafe actions, which may lead to…
UCB-based Algorithms for Multinomial Logistic Regression Bandits
Sanae Amani, Christos Thrampoulidis
Out of the rich family of generalized linear bandits, perhaps the most well studied ones are logisitc bandits that are used in problems with binary rewards: for instance, when the…
Decentralized Multi-Agent Linear Bandits with Safety Constraints
Sanae Amani, Christos Thrampoulidis
We study decentralized stochastic linear bandits, where a network of agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a -dimensional…
Regret Bounds for Safe Gaussian Process Bandit Optimization
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Many applications require a learner to make sequential decisions given uncertainty regarding both the system's payoff function and safety constraints. In safety-critical systems, i…
Safe Linear Thompson Sampling with Side Information
Ahmadreza Moradipari, Sanae Amani, Mahnoosh Alizadeh +1
The design and performance analysis of bandit algorithms in the presence of stage-wise safety or reliability constraints has recently garnered significant interest. In this work, w…
Linear Stochastic Bandits Under Safety Constraints
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Bandit algorithms have various application in safety-critical systems, where it is important to respect the system constraints that rely on the bandit's unknown parameters at every…