Highway-env

- fix vehicle order in occupancy grid obs
- fix broken seeding implementation
- support numpy types for discrete actions
- use Runge-Kutta 4 integration for dynamical continuous actions, making the dynamics make more stable
- use gymnasium rather than gym

Fixes https://github.com/eleurent/highway-env/issues/400

- Change the step / reset / render interfaces to match the new API of gym 0.26
- Drop support for gym <0.26

- fix a bug in generating discrete actions from continuous actions
- fix more bugs related to changes in gym's latest versions
- new intersection-env variant with continuous actions
- add longitudinal/lateral/angular offsets to the lane as part of the kinematics observation's features
- add more configurable options for reward function and termination conditions
- add configurable min/max speed for continuous actions
- bug fix for reward computation in the multi-agent setting
- add get_available_actions for MultiAgentAction
- fix various deprecation warnings
- add a multi-objective version of HighwayEnv

Huge thanks to contributors zerongxi, TibiGG, KexianShen, lorandcheng

- Add documentation on continuous actions
- Fix various bugs or imprecision in collision checks and obstacles rendering
- Image observations are now centered on the observer vehicle
- Fix the lane change behaviour in some situations
- Add `TupleObservation`, which is a union of several observation types
- Improve the accuracy of the `LidarObservation`
- Add support for `PolyLane`, and methods to save/load road networks from a config
- Fix steering wheel / angle conversion
- Change of the velocity term projection in the reward function
- Add support for latest gym versions (>=0.22) which dropped the Monitor wrapper
- Add a copy of the `GoalEnv` interface which was removed from gym

This release introduces additional content:
- a new continuous control environment, `racetrack-v0`, where the agent must learn to steer and follow the tracks, while avoiding other vehicles
- a new `"on_road"` layer in the `OccupancyGrid` observation type, which enables the observer to see the drivable space
- a new `"align_to_vehicle_axes"` option in the `OccupancyGrid` observation type, which renders the observation in the local vehicle frame
- a new `DiscreteAction` action type, which discretizes the original `ContinuousAction` type. This allows to do low-level control, but with a small discrete action space (e.g. for DQN). Note that this is different from the `DiscreteMetaAction` type, which implements its own low-level sub-policies.
- new example scripts and notebooks for training agents, such as a PPO continuous control policy for racetrack-v0.
- updated documentation