On the model-based stochastic value gradient for continuous reinforcement learning

Model-based reinforcement learning approaches add explicit domain knowledge to agents in hopes of improving the sample-efficiency in comparison to model-free agents. However, in practice model-based methods are unable to achieve the same asymptotic performance on challenging continuous control tasks due to the complexity of learning and controlling an explicit world model... (read more)

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METHOD TYPE
Entropy Regularization
Regularization