Adding documentation for Acrobot-v1 in docs/classic_control
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+Acrobot-v1
+---
+|Title|Action Type|Action Shape|Action Values|Observation Type| Observation Shape|Observation Values|Average Total Reward|Import|
+| ----------- | -----------| ----------- | -----------|-----------| ----------- | -----------| ----------- | -----------|
+|Acrobot-v1|Discrete|(2,)|(0,1,2)| Box |(6,)|[(-1,1),(-1,1), (-1, 1), (-1, 1), (-12.567, 12.567), (-28.274, 28.274)]| |`from gym.envs.classic_control import acrobot`|
+
+---
+
+### Description
+The Acrobot system includes two joints and two links, where the joint between the two links is actuated. Initially, the
+links are hanging downwards, and the goal is to swing the end of the lower link up to a given height by applying changes
+to torque on the actuated joint (middle).
+
+
+![Acrobot Episode Example](./acrobot.png)
+
+**Image**: two blue pendulum links connected by two green joints. The joint in between the two pendulum links is acted 
+upon by the agent via changes in torque. The goal is to swing the end of the outer-link to reach the target height 
+(black horizontal line above system).
+
+### Action Space
+
+The action is either applying +1, 0 or -1 torque on the joint between the two pendulum links.
+
+| Num | Action                 |
+|-----|------------------------|
+| 0   | apply -1 torque to the joint |
+| 1   | apply 0 torque to the joint |
+| 2   | apply 1 torque to the joint |
+
+### Observation Space
+
+The observation space gives information about the two rotational joint angles `theta1` and `theta2`, as well as their 
+angular velocities:
+- `theta1` is the angle of the inner link joint, where an angle of 0 indicates the first link is pointing directly 
+downwards.
+- `theta2` is *relative to the angle of the first link.* An angle of 0 corresponds to having the same angle between the 
+two links.
+
+The angular velocities of `theta1` and `theta2` are bounded at ±4π, and ±9π respectively.
+The observation is a `ndarray` with shape `(6,)` where the elements correspond to the following:
+
+| Num | Observation           | Min                  | Max                |
+|-----|-----------------------|----------------------|--------------------|
+| 0   | Cosine of `theta1`         | -1                 | 1                |
+| 1   | Sine of `theta1`         | -1                 | 1                |
+| 2   | Cosine of `theta2`            | -1 | 1 |
+| 3   | Sine of `theta2`            | -1 | 1 |
+| 4   | Angular velocity of `theta1` |        ~ -12.567 (-4 * pi)         |      ~ 12.567 (4 * pi)   |
+| 5   | Angular velocity of `theta2` |        ~ -28.274 (-9 * pi)         |      ~ 28.274 (9 * pi)   |
+
+or `[cos(theta1) sin(theta1) cos(theta2) sin(theta2) thetaDot1 thetaDot2]`. As an example, a state of 
+`[1, 0, 1, 0, ..., ...]` indicates that both links are pointing downwards.
+
+### Rewards
+
+All steps that do not reach the goal (termination criteria) incur a reward of -1. Achieving the target height and 
+terminating incurs a reward of 0. The reward threshold is -100.
+
+### Starting State
+
+At start, each parameter in the underlying state (`theta1`, `theta2`, and the two angular velocities) is initialized 
+uniformly at random between -0.1 and 0.1. This means both links are pointing roughly downwards.
+
+### Episode Termination
+The episode terminates of one of the following occurs:
+
+1. The target height is achieved. As constructed, this occurs when 
+`-cos(theta1) - cos(theta2 + theta1) > 1.0`
+2. Episode length is greater than 500 (200 for v0)
+
+### Arguments
+
+There are no arguments supported in constructing the environment. As an example:
+
+```python
+import gym
+env_name = 'Acrobot-v1'
+env = gym.make(env_name)
+```
+
+By default, the dynamics of the acrobot follow those described in Richard Sutton's book 
+[Reinforcement Learning: An Introduction](http://incompleteideas.net/book/11/node4.html). However, a `book_or_nips` 
+setting can be modified on the environment to change the pendulum dynamics to those described
+in [the original NeurIPS paper](https://papers.nips.cc/paper/1995/hash/8f1d43620bc6bb580df6e80b0dc05c48-Abstract.html). 
+See the following note and 
+the [implementation](https://github.com/openai/gym/blob/master/gym/envs/classic_control/acrobot.py) for details:
+
+> The dynamics equations were missing some terms in the NIPS paper which
+        are present in the book. R. Sutton confirmed in personal correspondence
+        that the experimental results shown in the paper and the book were
+        generated with the equations shown in the book.
+        However, there is the option to run the domain with the paper equations
+        by setting `book_or_nips = 'nips'`
+
+Continuing from the prior example:
+```python
+# To change the dynamics as described above
+env.env.book_or_nips = 'nips'
+```
+
+
+### Version History
+
+- v1: Maximum number of steps increased from 200 to 500. The observation space for v0 provided direct readings of 
+`theta1` and `theta2` in radians, having a range of `[-pi, pi]`. The v1 observation space as described here provides the
+sin and cosin of each angle instead.
+- v0: Initial versions release (1.0.0) (removed from openai/gym for v1)
+
+### References
+- Sutton, R. S. (1996). Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. In D. Touretzky, M. C. Mozer, & M. Hasselmo (Eds.), Advances in Neural Information Processing Systems (Vol. 8). MIT Press. https://proceedings.neurips.cc/paper/1995/file/8f1d43620bc6bb580df6e80b0dc05c48-Paper.pdf
+- Sutton, R. S., Barto, A. G. (2018 ). Reinforcement Learning: An Introduction. The MIT Press.
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Added some docs! 

I noticed `Acrobot-v0` was removed rather than deprecated, I believe in [this commit](https://github.com/openai/gym/commit/5ba5cb7e2dd7fb06412b0ba26fdba14127e87bbf). In the version history I mentioned the functional changes but did not mention the change from direct angle readings to sin/cos pairings provided by the v1 observation space. Let me know if you think there's a better way to handle (there's also a stale issue on [bringing `Acrobot-v0` back](https://github.com/openai/gym/issues/353))