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Task diversity is supposedly key to generalization in RL. But what does it do to continual RL, where agents face one new task distribution after another? We find that past a point, more diversity actually inhibits continual reinforcement learning 🧵

Wilka Carvalho

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15,333 次观看 • 5 个月前 •via X (Twitter)

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Wilka Carvalho 的头像

Wilka Carvalho5 个月前

In ML, diversity is the standard route to generalization. For example, domain randomization has shown that visual diversity enables zero-shot sim-to-real and XLand showed that agents trained on diverse tasks could then solve millions of new ones zero-shot.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

Interestingly, diversity has even been key to cognitive science results. Lake & Baroni showed that the right diverse task distribution induced systematicity in neural networks! A question open since Fodor & Pylyshyn. But nobody has studied what diversity does to continual learning.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

So we built Banyan: a procedurally-generated environment where you can systematically vary the components that define a task. A Banyan task is composed of a layout & a task tree, where each tree is defined by its topology and the objects assigned to its nodes.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

Vary any single component and agents begin a new distribution at or above the same performance that they finished the previous one with! This is true for both PPO and PQN.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

We analyze backward transfer and find that varying the number of layouts or object assignments has no effect, but varying the number of topologies has a big effect! Again, this is true for both PPO and PQN.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

So what happens across a sequence of ten distributions? We co-vary the number of layouts with the number of topologies, and find...

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

Task diversity inhibits continual reinforcement learning! At medium diversity (16 layouts × 16 topologies), performance climbs with each new distribution. But past that point, it monotonically gets worse.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

Strikingly, higher diversity improves backward transfer to the very first distribution! So the agent keeps getting better on old tasks even as it stops improving on new ones. This suggests it's learning the shared structure, but losing the ability to specialize.

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

If you want to learn more, or see the rest of our analysis, please check out our paper! 📄 Paper: 🌐 Project page: This was a fun collaboration with Purab Seth, @neilhshah15, @gershbrain, @kjha02, and @maxhkw !

Sam Z Liu 的头像

Sam Z Liu4 个月前

Could another view on this be that RL has a saturation point on its ability to learn tasks and early task diversity saturates that more quickly? Because the improvement in earlier tasks even at the later steps suggests that diversity is somehow still beneficial

Wilka Carvalho 的头像

Wilka Carvalho4 个月前

That makes sense! Yeah, one explanation is that this could be the primacy bias in effect

Stephanie Chan 的头像

Stephanie Chan4 个月前

@samzliu Maybe also loss of plasticity?

Wilka Carvalho 的头像

Wilka Carvalho4 个月前

@samzliu Thank you for this pointer!

mint yolk 的头像

mint yolk5 个月前

Very cool! Do you have an idea of how soon the code will be released?

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

aiming to have the code released in 2 months!

mint yolk 的头像

mint yolk5 个月前

😭😭 thank you anyway haha

Wilka Carvalho 的头像

Wilka Carvalho5 个月前

code isn't cleaned up so if you don't mind working with a messier version of the code and want to use it on a project, DM me! students I worked with are busy this summer so limited on time

mint yolk 的头像

mint yolk4 个月前

DM’d

Wilka Carvalho 的头像

Wilka Carvalho4 个月前

don't see the DM

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