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title: Progress_11/28

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Progress_11/28
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## Ways to prevent the extrapolation errors.
- What is **extrapolation errors**?
    1. The estimation error of actions that the policy might select but are <font color="#3338FF">not contained in the training dataset</font> would be <font color="#FF0000">overestimated</font>. 
    2. Thus, affects the policy improvement,where agents learn to prefer OOD actions whose value has been overestimated, and result in a poor performance.

- **Behavior regularization** is a prominent approach to offline RL that aims to address this problem by using appropriate regularizers to <font color="#3338FF">compel the learned policy to stay close to the data</font>.

- There are two common ways to incorporate behavior regularization into the actor-critic framework – via <font color="#FF0000">policy regularization</font> or via a <font color="#FF0000">critic penalty</font>.

- **Policy Regularization / Policy Constraints**
1. [Batch-Constrained deep Q- Learning(BCQ)](https://arxiv.org/pdf/1812.02900.pdf)
-- Constraint actions that the agent could select via using VAE to generate actions similar to that in the training data.
--  The perturbation model, ξ, used to enhence the diversity of actions.
![](https://i.imgur.com/bTzyMV0.png)
![](https://i.imgur.com/S1gse3G.png)

- **Critic Penalty**

1. [Conservative Q-Learning(CQL)](https://proceedings.neurips.cc/paper/2020/file/0d2b2061826a5df3221116a5085a6052-Paper.pdf)
-- Add some regularization term to the Bellman error, to make the learned Q-value is the lower-bound of the real Q-value .

![](https://i.imgur.com/9YydPLb.png)


- **Approximation**

![](https://i.imgur.com/Jkin8FI.png)
