Empirical results demonstrate that with an appropriately selected behavior policy we can estimate the policy gradient more accurately. The results also motivate ...
The ability to learn from off-policy data – data generated from past interaction with the environment – is essential to data efficient reinforcement ...
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Mar 15, 2018 · Empirical results demonstrate that with an appropriately selected behavior policy we can estimate the policy gradient more accurately. The ...
Peter Stone: Towards a Data Efficient Off-Policy Policy Gradient
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The ability to learn from off-policy data -- data generated from past interaction with the environment -- is essential to data efficient reinforcement learning.
In this paper we tackle this question by studying the efficient estimation of the policy gradient from off-policy data and the implications of this for learning ...
Feb 10, 2020 · In this paper, we consider the statistically efficient estimation of policy gradients from off-policy data, where the estimation is particularly non-trivial.
We show that conservative Q-Prop provides substantial gains in sample efficiency over trust region policy optimization (TRPO) with generalized advantage ...
Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value.
When aiming for data efficiency, we demonstrate the importance of off-policy optimization, as even flat policies trained off-policy can outperform on-policy ...
Nov 4, 2020 · Off-policy algorithms are sampling trajectory from a different policy than the policy(target policy) it optimises for. This can be linked with importance ...