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Found 4 papers, 1 papers with code
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations
Current implementations exhibit poor performance due to challenges such as irregular memory accesses and thread-level synchronization overheads on CPU.
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning
This leads to large overestimations of the Q values and performance deterioration of the learned policy.
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning
The goal of Offline Reinforcement Learning (RL) is to address this problem by learning effective policies using previously collected datasets.
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
Furthermore, we propose to generate \emph{diverse} model rollouts by non-uniform sampling of the environment states such that the entropy of the model rollouts is maximized.
Model-based Reinforcement Learning
reinforcement-learning
+1
