Marc Gendron-Bellemare

Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In th… (voir plus)is work, we provide a fresh perspective on these behaviors by studying the return landscape: the mapping between a policy and a return. We find that popular algorithms traverse noisy neighborhoods of this landscape, in which a single update to the policy parameters leads to a wide range of returns. By taking a distributional view of these returns, we map the landscape, characterizing failure-prone regions of policy space and revealing a hidden dimension of policy quality. We show that the landscape exhibits surprising structure by finding simple paths in parameter space which improve the stability of a policy. To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy. Taken together, our results provide new insight into the optimization, evaluation, and design of agents.

Small batch deep reinforcement learning

Johan Samir Obando Ceron

In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each … (voir plus)gradient update. Although critical to the learning process, this value is typically not adjusted when proposing new algorithms. In this work we present a broad empirical study that suggests {\em reducing} the batch size can result in a number of significant performance gains; this is surprising, as the general tendency when training neural networks is towards larger batch sizes for improved performance. We complement our experimental findings with a set of empirical analyses towards better understanding this phenomenon.

Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Colton Bishop

Maxim Ziatdinov

Igor Mordatch

Ekin Dogus Cubuk

Sergei V Kalinin

2023-07-22

Microscopy and Microanalysis (publié)

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

Bootstrapped Representations in Reinforcement Learning

Charline Le Lan

Stephen Tu

Mark Rowland

Anna Harutyunyan

Will Dabney

In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of… (voir plus) deep learning algorithms is to automatically construct features well-tuned for the task they try to solve, such a representation might not emerge from end-to-end training of deep RL agents. To mitigate this issue, auxiliary objectives are often incorporated into the learning process and help shape the learnt state representation. Bootstrapping methods are today's method of choice to make these additional predictions. Yet, it is unclear which features these algorithms capture and how they relate to those from other auxiliary-task-based approaches. In this paper, we address this gap and provide a theoretical characterization of the state representation learnt by temporal difference learning (Sutton, 1988). Surprisingly, we find that this representation differs from the features learned by Monte Carlo and residual gradient algorithms for most transition structures of the environment in the policy evaluation setting. We describe the efficacy of these representations for policy evaluation, and use our theoretical analysis to design new auxiliary learning rules. We complement our theoretical results with an empirical comparison of these learning rules for different cumulant functions on classic domains such as the four-room domain (Sutton et al, 1999) and Mountain Car (Moore, 1990).

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces

Charline Le Lan

Joshua Greaves

Jesse Farebrother

Mark Rowland

Fabian Pedregosa

In this paper, we derive an algorithm that learns a principal subspace from sample entries, can be applied when the approximate subspace i… (voir plus)s represented by a neural network, and hence can bescaled to datasets with an effectively infinite number of rows and columns. Our method consistsin defining a loss function whose minimizer is the desired principal subspace, and constructing agradient estimate of this loss whose bias can be controlled.

2023-04-11

Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (publié)

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning

Adrien Ali Taiga

Jesse Farebrother

Google Brain

2023-02-01

ICLR.cc/2023/Conference (poster)

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Jesse Farebrother

Joshua Greaves

Charline Le Lan

Ross Goroshin

Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well-und… (voir plus)erstood; in practice, how-ever, their primary use remains in support of a main learning objective, rather than as a method for learning representations. This is perhaps surprising given that many auxiliary tasks are defined procedurally, and hence can be treated as an essentially infinite source of information about the environment. Based on this observation, we study the effectiveness of auxiliary tasks for learning rich representations, focusing on the setting where the number of tasks and the size of the agent’s network are simultaneously increased. For this purpose, we derive a new family of auxiliary tasks based on the successor measure. These tasks are easy to implement and have appealing theoretical properties. Combined with a suitable off-policy learning rule, the result is a representation learning algorithm that can be understood as extending Mahadevan & Maggioni (2007)’s proto-value functions to deep reinforcement learning – accordingly, we call the resulting object proto-value networks. Through a series of experiments on the Arcade Learning Environment, we demonstrate that proto-value networks produce rich features that may be used to obtain performance comparable to established algorithms, using only linear approximation and a small number (~4M) of interactions with the environment’s reward function.

2023-02-01

ICLR.cc/2023/Conference (poster)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier

Pierluca D'Oro

Max Schwarzer

Evgenii Nikishin

Pierre-Luc Bacon

Increasing the replay ratio, the number of updates of an agent's parameters per environment interaction, is an appealing strategy for improv… (voir plus)ing the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents causes better replay ratio scaling capabilities to emerge. We push the limits of the sample efficiency of carefully-modified algorithms by training them using an order of magnitude more updates than usual, significantly improving their performance in the Atari 100k and DeepMind Control Suite benchmarks. We then provide an analysis of the design choices required for favorable replay ratio scaling to be possible and discuss inherent limits and tradeoffs.

2023-02-01

ICLR.cc/2023/Conference (notable)

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Samir Obando Ceron

2023-01-01

ICML (publié)

The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning

Johan Samir Obando Ceron

2023-01-01

Tiny Papers @ ICLR (published)

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

Mark Rowland

Yunhao Tang

Clare Lyle

Remi Munos

Will Dabney

We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distri… (voir plus)butional reinforcement learning algorithm, quantile temporal-difference learning (QTD), for this task. We reach the surprising conclusion that even if a practitioner has no interest in the return distribution beyond the mean, QTD (which learns predictions about the full distribution of returns) may offer performance superior to approaches such as classical TD learning, which predict only the mean return, even in the tabular setting.

2023-01-01

ICML (publié)