Search Results for author: Grégoire Delétang

Found 5 papers, 0 papers with code

Your Policy Regularizer is Secretly an Adversary

no code implementations23 Mar 2022 Rob Brekelmans, Tim Genewein, Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro Ortega

Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy.

reinforcement-learning

Model-Free Risk-Sensitive Reinforcement Learning

no code implementations4 Nov 2021 Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A. Ortega

Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.

Decision Making reinforcement-learning

Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning

no code implementations NeurIPS 2021 Grégoire Delétang, Jordi Grau-Moya, Markus Kunesch, Tim Genewein, Rob Brekelmans, Shane Legg, Pedro A Ortega

Since the Gaussian free energy is known to be a certainty-equivalent sensitive to the mean and the variance, the learning rule has applications in risk-sensitive decision-making.

Decision Making reinforcement-learning

Meta-trained agents implement Bayes-optimal agents

no code implementations NeurIPS 2020 Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega

Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.

Meta-Learning

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