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Why is soft actor critic off policy?
Soft Actor Critic, or SAC, is an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible.
What is on policy and off policy?
On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data.
What is the difference between on-policy and off policy?
How does the critic and the actor work?
The “Critic” estimates the value function. This could be the action-value (the Q value) or state-value (the V value ). The “Actor” updates the policy distribution in the direction suggested by the Critic (such as with policy gradients). and both the Critic and Actor functions are parameterized with neural networks.
How are critic and actor functions parameterized with neural networks?
The “Actor” updates the policy distribution in the direction suggested by the Critic (such as with policy gradients). and both the Critic and Actor functions are parameterized with neural networks. In the derivation above, the Critic neural network parameterizes the Q value — so, it is called Q Actor Critic.
What is the pseudocode for Q actor critic?
In the derivation above, the Critic neural network parameterizes the Q value — so, it is called Q Actor Critic. Below is the pseudocode for Q-Actor-Critic: As illustrated, we update both the Critic network and the Value network at each update step.
How does the reinforce algorithm update the policy gradient?
Recall the policy gradient: As in the REINFORCE algorithm, we update the policy parameter through Monte Carlo updates (i.e. taking random samples).