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Found 9 papers, 1 papers with code
Enhancing Reinforcement Learning Agents with Local Guides
This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent.
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark
This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment.
Differentially Private Model-Based Offline Reinforcement Learning
We address offline reinforcement learning with privacy guarantees, where the goal is to train a policy that is differentially private with respect to individual trajectories in the dataset.
A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning
In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer.
Clustered Multi-Agent Linear Bandits
We address in this paper a particular instance of the multi-agent linear stochastic bandit problem, called clustered multi-agent linear bandits.
Price of Safety in Linear Best Arm Identification
We introduce the safe best-arm identification framework with linear feedback, where the agent is subject to some stage-wise safety constraint that linearly depends on an unknown parameter vector.
Density Estimation for Conservative Q-Learning
We hence introduce Density Conservative Q-Learning (D-CQL), a batch-RL algorithm with strong theoretical guarantees that carefully penalizes the value function based on the amount of information collected in the state-action space.
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration
This novel smoothing method is then used to improve first-order non-smooth optimization (both convex and non-convex) by allowing for a local exploration of the search space.
