To correctly identify the reward function, we require As- sumption 1, which stipulates that there exists an anchor action aA whose reward function value is known a priori. A special case is g(s)=0, indicating that there exists an an- chor action providing no rewards.
Abstract. We propose a reward function estimation frame- work for inverse reinforcement learning with deep energy-based policies. We name our method PQR,.
Our method sequentially estimates the policy, the Q -function, and the reward. We refer to it as the PQR method. This method does not require the assumption ...
Jul 15, 2020 · We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies.
This work proposes a reward function estimation framework for inverse reinforcement learning with deep energy-based policies, and names the method PQR, ...
We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR, ...
This work proposes a Policy Q-function Reward (PQR) approach, combined with an anchor-action assumption, to identify and flexibly estimate reward functions in ...
Jul 15, 2020 · We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR ...
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Jul 12, 2020 · We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies.