Sheeprl

- Added parallel stochastic in dv3: 225
- Update dependencies and python version: 230, 262, 263
- Added dv3 notebook for imagination and obs reconstruction: 232
- Created citation.cff: 233
- Added replay ratio for off-policy algorithms: 247
- Single strategy for the player (now it is instantiated in the `build_agent()` function: 244, 250, 258
- Proper `terminated` and `truncated` signals management: 251, 252, 253
- Added the possibility to choose whether or not to learn initial recurrent state: 256
- Added A2C benchmarks: 266
- Added `prepare_obs()` function to all the algorithms: 267
- Improved code readability: 248, 265
- bug fix: 220, 222, 224, 231, 243, 255, 257

* Added Dreamer V3 different sizes configs (208).
* Update torch version: 2.2.1 or in [2.0.*, 2.1.*] (212).
* Fix observation normalization in dreamer v3 and p2e_dv3 (214).
* Update README (215).
* Fix installation and agent evaluation: new commands are made available for agent evaluation, model registration, and for the available agents (216).

* Added benchmarks (185)
* Added possibility to use a user-defined evaluation file (199)
* Let the user choose for num_threads and matmul precision (203)
* Added Super Mario Bros Environment (204)
* Fix bugs (183, 186, 193, 195, 200, 201, 202, 205)

* Added A2C algorithm (33).
* Added a new how-to on how to add an external algorithm (no need to clone sheeprl locally) in (175).
* Added optimizations (177):
* Metrics are instantiated only when needed.
* Removed the `torch.cat()` operation between empty and dense tensors in the `MultiEncoder` class.
* Added possibility not to test the agent after training.
* Fixed GitHub actions workflow (180).
* Fixed bugs (181, 183).
* Added benchmarks with respect to StableBaselines3 (185).
* Added `BernoulliSafeMode` distribution, which is a Bernoulli distribution where the mode is computed safely, i.e. it returns `self.probs > 0.5` without seeting any NaN (186) .

* Fix bugs (174).

* Added Numpy buffers (169):
* The user can now decide if to use the `torch.as_tensor` function or the `torch.from_numpy` one to convert the Numpy buffer into tensors when sampling (172).
* Added optimizations to reduce training time (168).
* Added the possibility to keep only the last `n` checkpoints in an experiment to avoid filling up the disk (171).
* Fix bugs (167).
* Update documentation.