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Found 7 papers, 1 papers with code
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
Exploration via linearly perturbed loss minimisation
We show that, for the case of generalised linear bandits, EVILL reduces to perturbed history exploration (PHE), a method where exploration is done by training on randomly perturbed rewards.
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle.
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning
Soft-greedy operators, namely $\varepsilon$-greedy and softmax, remain a common choice to induce a basic level of exploration for action-value methods in reinforcement learning.
An Elementary Proof that Q-learning Converges Almost Surely
Watkins' and Dayan's Q-learning is a model-free reinforcement learning algorithm that iteratively refines an estimate for the optimal action-value function of an MDP by stochastically "visiting" many state-ation pairs [Watkins and Dayan, 1992].
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle.
Model-Based Reinforcement Learning with Value-Targeted Regression
We propose a model based RL algorithm that is based on optimism principle: In each episode, the set of models that are `consistent' with the data collected is constructed.
