v0.2.0

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Added
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* Introduce cherry.nn.Policy, cherry.nn.ActionValue, and cherry.nn.StateValue.
* Algorithm class utilities for: A2C, PPO, TRPO, DDPG, TD3, SAC, and DrQ/DrQv2.
* DMC examples for SAC, DrQ, and DrQv2.
* N-steps returns sampling in ExperienceReplay.

Changed
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* Discontinue most of cherry.wrappers.

Fixed
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* Fixes return value of StateNormalizer and RewardNormalizer wrappers.
* Requirements to generate docs.

v0.1.4

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Fixed
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* Support for torch 1.5 and new `_parse_to` behavior in ExperienceReplay. (thanks @ManifoldFR)

v0.1.3

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Added
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* A CHANGELOG.md file.

Changed
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* Travis testing with different versions of Python (3.6, 3.7), torch (1.1, 1.2, 1.3, 1.4), and torchvision (0.3, 0.4, 0.5).

Fixed
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* Bugfix when using `td.discount` with replays coming from vectorized environments (@galatolofederico)
* env.action_size and env.state_size when the number of vectorized environments is 1. (thanks @galatolofederico)
* Actor-critic integration test being to finicky.
* `cherry.onehot` support for numpy's float and integer types. (thanks @ngoby)

Merge branch 'dev'