Jul 9, 2017 · Learning optimal policies under stochastic rewards presents a challenge for well-known reinforcement learning algorithms such as Q-learning.
The smooth actor-critic algorithm for both deterministic policy and stochastic policy systems is proposed, with a regularization term added to the objective ...
Learning optimal policies under stochastic rewards presents a challenge for well-known reinforcement learning algorithms such as Q-learning.
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Feb 22, 2022 · They have inherently deterministic policies to which we add noise for exploration, which makes the resultant policy stochastic. The advantage in ...
In practice, the deterministic actor-critic significantly outperformed its stochastic counterpart by several orders of magnitude in a bandit with 50 continuous ...
Nov 12, 2019 · It is perfectly reasonable to use a critic to reduce variance and this is what for example Deep Deterministic Policy Gradient (DDPG) does.
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Sep 13, 2022 · This paper presents a comparative analysis of two Deep-RL techniques - Deep Deterministic Policy Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing ...
Nov 18, 2018 · Deterministic policy means that for every state you have a clear defined action you will take. · Stochastic policy means that for every state, ...
Feb 15, 2023 · A deterministic policy will always select an action given some state. The stochastic policy will sample the action given a state.
It also employs the actor-critic approach. The critic predicts the agent's rewards for the given observation, whereas the actor maps the observation to action.
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