Refactor PPG and PPO for procgen

[vwxyzjn]

This PR refactors PPO and PPG. Specifically,

In PPO:

• Match the learning rate of 5e-4: see the screenshot from the procgen paper.
• Add reward normalization per the screenshot from the procgen paper.
• Add layer initialization for output layers: while the IMPALA network itself uses the default layer initialization (common/models.py#L28-L69), the actor and critics head still uses the corresponding initialization.
• Change the distribution mode from hard to easy (save compute) and correspondingly change total_timesteps from 100M to 25M.
• Turn off learning rate annealing by default per the screenshot
  

In PPG:

• Refactor according to the new format
• Rescale the image by diving 255 (previously not done)

In light of this, we would also require a new benchmark. Starting a PR to track this progress.

[gitpod-io]

[vwxyzjn]

This report tracks the performance: https://wandb.ai/costa-huang/cleanRL/reports/Procgen-Our-PPO-vs-openai-baselines-PPO--VmlldzoxNTIyNjA5

[vwxyzjn]

Based on results from https://wandb.ai/costa-huang/cleanRL/reports/Procgen-Our-PPO-vs-openai-baselines-PPO--VmlldzoxNTIyNjA5, this was a successful refactoring. Merging now