🦎 New strategies, API flexibility, small fixes

Added

• Adds a total_env_steps counter to both GymFitness and BraxFitness for easier sample efficiency comparability with RL algorithms.
• Support for new strategies/genetic algorithms
  • SAMR-GA (Clune et al., 2008)
  • GESMR-GA (Kumar et al., 2022)
  • SNES (Wierstra et al., 2014)
  • DES (Lange et al., 2022)
  • Guided ES (Maheswaranathan et al., 2018)
  • ASEBO (Choromanski et al., 2019)
  • CR-FM-NES (Nomura & Ono, 2022)
  • MR15-GA (Rechenberg, 1978)
• Adds full set of BBOB low-dimensional functions (BBOBFitness)
• Adds 2D visualizer animating sampled points (BBOBVisualizer)
• Adds Evosax2JAXWrapper to wrap all evosax strategies
• Adds Adan optimizer (Xie et al., 2022)

Changed

• ParameterReshaper can now be directly applied from within the strategy. You simply have to provide a pholder_params pytree at strategy instantiation (and no num_dims).
• FitnessShaper can also be directly applied from within the strategy. This makes it easier to track the best performing member across generations and addresses issue #32. Simply provide the fitness shaping settings as args to the strategy (maximize, centered_rank, ...)
• Removes Brax fitness (use EvoJAX version instead)
• Add lrate and sigma schedule to strategy instantiation

Fixed

• Fixed reward masking in GymFitness. Using jnp.sum(dones) >= 1 for cumulative return computation zeros out the final timestep, which is wrong. That's why there were problems with sparse reward gym environments (e.g. Mountain Car).
• Fixed PGPE sample indexing.
• Fixed weight decay. Falsely multiplied by -1 when maximization.