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Found 10 papers, 0 papers with code
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing
Our research demonstrates that to achieve $\epsilon$-optimal policies for all $M$ tasks, a single agent using DistMT-LSVI needs to run a total number of episodes that is at most $\tilde{\mathcal{O}}({d^3H^6(\epsilon^{-2}+c_{\rm sep}^{-2})}\cdot M/N)$, where $c_{\rm sep}>0$ is a constant representing task separability, $H$ is the horizon of each episode, and $d$ is the feature dimension of the dynamics and rewards.
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation
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 policy while solving a streaming sequence of tasks.
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
In particular, for scenarios with known context distribution, the communication cost of DisBE-LUCB is only $\tilde{\mathcal{O}}(dN)$ and its regret is ${\tilde{\mathcal{O}}}(\sqrt{dNT})$, which is of the same order as that incurred by an optimal single-agent algorithm for $NT$ rounds.
Safe Reinforcement Learning with Linear Function Approximation
Safety in reinforcement learning has become increasingly important in recent years.
UCB-based Algorithms for Multinomial Logistic Regression Bandits
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 learner/agent tries to maximize the profit over a user that can select one of two possible outcomes (e. g., `click' vs `no-click').
Decentralized Multi-Agent Linear Bandits with Safety Constraints
For this problem, we propose DLUCB: a fully decentralized algorithm that minimizes the cumulative regret over the entire network.
Regret Bound for Safe Gaussian Process Bandit Optimization
Many applications require a learner to make sequential decisions given uncertainty regarding both the system’s payoff function and safety constraints.
Regret Bounds for Safe Gaussian Process Bandit Optimization
Many applications require a learner to make sequential decisions given uncertainty regarding both the system's payoff function and safety constraints.
Safe Linear Thompson Sampling with Side Information
We compare the performance of our algorithm with UCB-based safe algorithms and highlight how the inherently randomized nature of TS leads to a superior performance in expanding the set of safe actions the algorithm has access to at each round.
Linear Stochastic Bandits Under Safety Constraints
During the pure exploration phase the learner chooses her actions at random from a restricted set of safe actions with the goal of learning a good approximation of the entire unknown safe set.
