Multi-task Deep Reinforcement Learning with PopArt Multi-task Deep Reinforcement Learning with PopArt
Paper summary This paper posits that one of the central problems stopping multi-task RL - that is, single models trained to perform multiple tasks well - from reaching better performance, is the inability to balance model resources and capacity between the different tasks the model is being asked to learn. Empirically, prior to this paper, multi-task RL could reach ~50% of human accuracy on Atari and Deepmind Lab tasks. The fact that this is lower than human accuracy is actually somewhat less salient than the fact that it’s quite a lot lower than single-task RL - how a single model trained to perform only that task could do. When learning a RL model across multiple tasks, the reward structures of the different tasks can vary dramatically. Some can have high-magnitude, sparse rewards, some can have low magnitude rewards throughout. If a model learns it can gain what it thinks is legitimately more reward by getting better at a game with an average reward of 2500 than it does with an average reward of 15, it will put more capacity into solving the former task. Even if you apply normalization strategies like reward clipping (which treats all rewards as a binary signal, regardless of magnitude, and just seeks to increase the frequency of rewards), that doesn’t deal with some environments having more frequent rewards than others, and thus more total reward when summed over timestep. The authors here try to solve this problem by performing a specific kind of normalization, called Pop Art normalization, on the problem. PopArt normalization (don’t worry about the name) works by adaptively normalizing both the target and the estimate of the target output by the model, at every step. In the Actor-Critic case that this model is working on, the target and estimate that are being normalized are, respectively, 1) the aggregated rewards of the trajectories from state S onward, and 2) the value estimate at state S. If your value function is perfect, these two things should be equivalent, and so you optimize your value function to be closer to the true rewards under your policy. And, then, you update your policy to increase probability of actions with higher advantage (expected reward with that action, relative to the baseline Value(S) of that state). The “adaptive” part of that refers to correcting for the fact when you’re estimating, say, a Value function to predict the total future reward of following a policy at a state, that V(S) will be strongly non-stationary, since by improving your policy you are directly optimizing to increase that value. This is done by calculating “scale” and “shift” parameters off of a recent data. The other part of the PopArt algorithm works by actually updating the estimate our model is producing, to stay normalized alongside the continually-being-re-normalized target. It does this by taking the new and old versions of scale (sigma) and shift (mu) parameters (which will be used to normalize the target) and updates the weights and biases of the last layer, such that the movement of the estimator moves along with the movement in the target. Using this toolkit, this paper proposes learning one *policy* that’s shared over all task, but keeping shared value estimation functions for each task. Then, it normalizes each task’s values independently, meaning that each task ends up contributing equal weight to the gradient updates of the model (both for the Value and Policy updates). In doing this, the authors find dramatically improved performance at both Atari and Deepmind, relative to prior IMPALA work
Multi-task Deep Reinforcement Learning with PopArt
Matteo Hessel and Hubert Soyer and Lasse Espeholt and Wojciech Czarnecki and Simon Schmitt and Hado van Hasselt
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, stat.ML


Summary by CodyWild 2 years ago
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