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Found 11 papers, 2 papers with code
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model.
Offline Reinforcement Learning as Anti-Exploration
This is the converse of exploration in RL, which favors such actions.
Offline Reinforcement Learning with Pseudometric Learning
In the presence of function approximation, and under the assumption of limited coverage of the state-action space of the environment, it is necessary to enforce the policy to visit state-action pairs close to the support of logged transitions.
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning
Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance.
Munchausen Reinforcement Learning
Bootstrapping is a core mechanism in Reinforcement Learning (RL).
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Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Leverage the Average: an Analysis of KL Regularization in RL
Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance.
Momentum in Reinforcement Learning
We adapt the optimization's concept of momentum to reinforcement learning.
On Connections between Constrained Optimization and Reinforcement Learning
In this paper, we draw connections between DP and (constrained) convex optimization.
Deep Conservative Policy Iteration
Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP).
