Pablo Samuel Castro

Current multi-task reinforcement learning (MTRL) methods have the ability to perform a large number of tasks with a single policy. However w… (voir plus)hen attempting to interact with a new domain, the MTRL agent would need to be re-trained due to differences in domain dynamics and structure. Because of these limitations, we are forced to train multiple policies even though tasks may have shared dynamics, leading to needing more samples and is thus sample inefficient. In this work, we explore the ability of MTRL agents to learn in various domains with various dynamics by simultaneously learning in multiple domains, without the need to fine-tune extra policies. In doing so we find that a MTRL agent trained in multiple domains induces an increase in sample efficiency of up to 70\% while maintaining the overall success rate of the MTRL agent.

2024-10-23

corl.org/2024/Workshop/MAPoDeL (publié)

Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL

Ghada Sokar

Johan Samir Obando Ceron

Hugo Larochelle

The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While sof… (voir plus)t mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reasons behind their effectiveness remain largely unknown. In this work we provide an in-depth analysis identifying the key factors driving this performance gain. We discover the surprising result that tokenizing the encoder output, rather than the use of multiple experts, is what is behind the efficacy of SoftMoEs. Indeed, we demonstrate that even with an appropriately scaled single expert, we are able to maintain the performance gains, largely thanks to tokenization.

2024-10-02

ArXiv (prépublication)

arxiv.org

CALE: Continuous Arcade Learning Environment

Jesse Farebrother

We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare … (voir plus)et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such as PPO [Schulman et al., 2017] and SAC [Haarnoja et al., 2018]) and value-based agents (such as DQN [Mnih et al., 2015] and Rainbow [Hessel et al., 2018]) on the same environment suite. We provide a series of open questions and research directions that CALE enables, as well as initial baseline results using Soft Actor-Critic. CALE is available as part of the ALE athttps://github.com/Farama-Foundation/Arcade-Learning-Environment.

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)

Adaptive Accompaniment with ReaLchords

Yusong Wu

Tim Cooijmans

Kyle Kastner

Adam Roberts

Ian Simon

Alexander Scarlatos

Chris Donahue

Cassie Tarakajian

Shayegan Omidshafiei

Natasha Jaques

Anna (Cheng-Zhi) Huang

Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expr… (voir plus)essive output but are not able to generate in an online manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use. The finetuning objective leverages both a novel reward model that provides feedback on both harmonic and temporal coherency between melody and chord, and a divergence term that implements a novel type of distillation from a teacher model that can see the future melody. Through quantitative experiments and listening tests, we demonstrate that the resulting model adapts well to unfamiliar input and produce fitting accompaniment. ReaLchords opens the door to live jamming, as well as simultaneous co-creation in other modalities.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Jesse Farebrother

Jordi Orbay

Quan Vuong

Adrien Ali Taiga

Yevgen Chebotar

Ted Xiao

Alex Irpan

Sergey Levine

Aleksandra Faust

Aviral Kumar

Rishabh Agarwal

Value functions are an essential component in deep reinforcement learning (RL), that are typically trained via mean squared error regression… (voir plus) to match bootstrapped target values. However, scaling value-based RL methods to large networks has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We show that training value functions with categorical cross-entropy significantly enhances performance and scalability across various domains, including single-task RL on Atari 2600 games, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that categorical cross-entropy mitigates issues inherent to value-based RL, such as noisy targets and non-stationarity. We argue that shifting to categorical cross-entropy for training value functions can substantially improve the scalability of deep RL at little-to-no cost.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

In value-based deep reinforcement learning, a pruned network is a good network

Johan Samir Obando Ceron

Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (voir plus)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables {value-based} agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters. Our code is publicly available, see Appendix A for details.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

proceedings.mlr.press

The Position Dependence of Electron Beam Induced Effects in 2D Materials with Deep Neural Networks

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Riccardo Torsi

Rishabh Agarwal

Colton Bishop

Igor Mordatch

Ekin Dogus Cubuk

Marc Gendron-Bellemare

Joshua Robinson

Sergei V Kalinin

2024-07-01

Microscopy and Microanalysis (publié)

Gintare Karolina Dziugaite

Mixture of Experts in a Mixture of RL settings

Timon Willi

Johan Samir Obando Ceron

Jakob Nicolaus Foerster

Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to … (voir plus)distributed training, and modularity. Previous research has illustrated that MoEs can significantly boost Deep Reinforcement Learning (DRL) performance by expanding the network's parameter count while reducing dormant neurons, thereby enhancing the model's learning capacity and ability to deal with non-stationarity. In this work, we shed more light on MoEs' ability to deal with non-stationarity and investigate MoEs in DRL settings with"amplified"non-stationarity via multi-task training, providing further evidence that MoEs improve learning capacity. In contrast to previous work, our multi-task results allow us to better understand the underlying causes for the beneficial effect of MoE in DRL training, the impact of the various MoE components, and insights into how best to incorporate them in actor-critic-based DRL networks. Finally, we also confirm results from previous work.

2024-06-26

ArXiv (prépublication)

arxiv.org

On the consistency of hyper-parameter selection in value-based deep reinforcement learning

Johan Samir Obando Ceron

João Guilherme Madeira Araújo

Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and car… (voir plus)eful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements built upon prior approaches, while hyper-parameter choices are typically inherited from previous methods or fine-tuned specifically for the proposed technique. Despite their crucial impact on performance, hyper-parameter choices are frequently overshadowed by algorithmic advancements. This paper conducts an extensive empirical study focusing on the reliability of hyper-parameter selection for value-based deep reinforcement learning agents, including the introduction of a new score to quantify the consistency and reliability of various hyper-parameters. Our findings not only help establish which hyper-parameters are most critical to tune, but also help clarify which tunings remain consistent across different training regimes.

2024-05-14

rl-conference.cc/RLC/2024/Conference (publié)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Doina Precup

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance s… (voir plus)cales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.

2024-05-01

ICML.cc/2024/Conference (spotlight)

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Jesse Farebrother

Jordi Orbay

Quan Vuong

Adrien Ali Taiga

Yevgen Chebotar

Ted Xiao

Alex Irpan

Sergey Levine

Aleksandra Faust

Aviral Kumar

Rishabh Agarwal

2024-05-01

ICML.cc/2024/Conference (présentation orale)

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Jesse Farebrother

Jordi Orbay

Quan Ho Vuong

Adrien Ali Taiga

Yevgen Chebotar

Ted Xiao

A. Irpan

Sergey Levine

Aleksandra Faust

Aviral Kumar

Rishabh Agarwal

Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained … (voir plus)using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost.

2024-03-06

ArXiv (prépublication)