pymdp 0.0.4

Updates include:

• Read the Docs / Sphinx-based documentation, with references thereto in a cleaned-up/shortened README.md
• new epistemic chaining demo (on README.md and in documentation)
• renaming of many inference.py and learning.py functions to make them more understandable

BUG FIXES:

• corrected action sampling routine (in pymdp.control.sample_action()) so that when marginalizing posterior probabilities from each policy, per action, we only sum the probabilities assigned to action for the next / successive timestep, not for all timesteps. Before this fix, this can lead to maladaptive behavior in many scenarios where temporally-deep planning is required.