Reward hacking in simple HTM agents (using OpenAI Gym)

[marty1885]

@Paul_Lamb

Adding temporal memory might help, since it would allow an agent to model more complex states (high order vs low order). Not sure this helps with the long term reword tracking though.

I didn’t add a TM to the agent because the world state is static and nothing is hidden to the agent. So there’s no need of memory. Hm… how could HTM perform reward tracking? I’ve been trying but it seems very difficult with the standard SP/TM and local learning rules. (And tracking rewards as a real number is also tricky)

@Paul_Lamb

Another thing I’ve been thinking about for a while is that, compared to traditional RL strategies, biological agents have another detail which probably drives more complex behavior – competing needs.

That’s a very good point!