Generally capable agents emerge from open-ended play
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Re: Generally capable agents emerge from open-ended play
#2"I was set upon this world to try and [outsmart] you, but this, is what I've become." - Beyond the walls of Eryx.
Re: Generally capable agents emerge from open-ended play
#3"Analysing the agent’s internal representations, we can say that by taking this approach to reinforcement learning in a vast task space, our agents are aware of the basics of their bodies and the passage of time and that they understand the high-level structure of the games they encounter."
Wow, really amazing if true.
P.S.: After looking into their paper, it's not that impressive. They use agent's internal states (LSTM cells, attention outputs, etc.) to predict whether it is early in the episode, or whether the agent is holding an object.
Re: Generally capable agents emerge from open-ended play
#4"Analysing the agent’s internal representations, we can say that by taking this approach to reinforcement learning in a vast task space, our agents are aware of the basics of their bodies and the passage of time and that they understand the high-level structure of the games they encounter." Wow, really amazing if true. P.S.: After looking into their paper, it's not that impressive. They use agent's internal states (L…
"Aware" is probably overly anthropomorphized language there. What they mean to say is that all these things have become parameterized within the model.
Re: Generally capable agents emerge from open-ended play
#5"Analysing the agent’s internal representations, we can say that by taking this approach to reinforcement learning in a vast task space, our agents are aware of the basics of their bodies and the passage of time and that they understand the high-level structure of the games they encounter." Wow, really amazing if true. P.S.: After looking into their paper, it's not that impressive. They use agent's internal states (L…
"Aware" is probably overly anthropomorphized language there. What they mean to say is that all these things have become parameterized within the model.
It would be interesting to see what would happen if they added social dynamics between the agents...like some space for theory of mind (what is that agent thinking), mimicry, communication, etc.
Re: Generally capable agents emerge from open-ended play
#6"Analysing the agent’s internal representations, we can say that by taking this approach to reinforcement learning in a vast task space, our agents are aware of the basics of their bodies and the passage of time and that they understand the high-level structure of the games they encounter." Wow, really amazing if true. P.S.: After looking into their paper, it's not that impressive. They use agent's internal states (L…
"Aware" is probably overly anthropomorphized language there. What they mean to say is that all these things have become parameterized within the model.
the main question of course being, aren't we anthropomorphizing ourselves too much ?
Re: Generally capable agents emerge from open-ended play
#7Agents trained in simulation like this often flail about seemingly randomly, and when they achieve their goals it seems almost accidental. Rather than this being some kind of limitation of the learning algorithm, I think it might be the optimal strategy, and humans would behave that way too if there was no such thing as fatigue or pain.
If we want agents to behave more realistically and move with more apparent intention we need cost functions that include a "pain" and/or fatigue term to penalize flailing behavior. But that adds hyperparameters that need to be carefully tuned to balance penalties with rewards, otherwise training will be unstable or simply fail.
I wonder if there's a principled way to determine an appropriate cost function without manual tuning. Did evolution serve as the "manual" optimizer that generated a precisely tuned cost function for the human brain? Or did evolution discover a generally applicable method for automatically generating cost functions, which the brain then applies to whatever input it gets?
Re: Generally capable agents emerge from open-ended play
#8Earlier quoted context omitted.
"Aware" is probably overly anthropomorphized language there. What they mean to say is that all these things have become parameterized within the model.
the main question of course being, aren't we anthropomorphizing ourselves too much ?
Asking the real questions that will upset alot of people. ;)
Re: Generally capable agents emerge from open-ended play
#9Agents trained in simulation like this often flail about seemingly randomly, and when they achieve their goals it seems almost accidental. Rather than this being some kind of limitation of the learning algorithm, I think it might be the optimal strategy, and humans would behave that way too if there was no such thing as fatigue or pain. If we want agents to behave more realistically and move with more apparent intent…
Watching my 3-year-old, I suspect humans do behave this way, and without regards to pain or fatigue. He'll hit himself on the head with duplos or bang his head against the wall just to see what happens. The pain is just one more signal for reinforcement learning.
I recall a paper in a non-CS journal (psych or neuroscience) that posited that the optimal way to gain large quantities of information about an uncertain environment is to simply perturb the environment in random ways and see what happens. Young children will often do lots of seemingly stupid or random things (see the r/KidsAreFuckingStupid subreddit) with the trust that if it's actually a life-ending decision their parent will stop them.
Re: Generally capable agents emerge from open-ended play
#10Agents trained in simulation like this often flail about seemingly randomly, and when they achieve their goals it seems almost accidental. Rather than this being some kind of limitation of the learning algorithm, I think it might be the optimal strategy, and humans would behave that way too if there was no such thing as fatigue or pain. If we want agents to behave more realistically and move with more apparent intent…
It's proven that if you want polynomial sample complexity in the size of the state space you need directed exploration. The algorithms 'flail' because they are initialized with random policies.