Johan Samir Obando Ceron

Lu Liu

Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure m… (voir plus)ode remain poorly understood. Several recent works have proposed mechanisms to address this, but they are often complex and fail to highlight the causes underlying this difficulty. In this work, we conduct a series of empirical analyses which suggest that the combination of non-stationarity with gradient pathologies, due to suboptimal architectural choices, underlie the challenges of scale. We propose a series of direct interventions that stabilize gradient flow, enabling robust performance across a range of network depths and widths. Our interventions are simple to implement and compatible with well-established algorithms, and result in an effective mechanism that enables strong performance even at large scales. We validate our findings on a variety of agents and suites of environments.

2025-06-01

arXiv (publié)

arxiv.org

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning

Jiashun Liu

Zihao Wu

Ling Pan

2025-05-01

arXiv (publié)

arxiv.org

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

Hongyao Tang

Glen Berseth

Plasticity, or the ability of an agent to adapt to new tasks, environments, or distributions, is crucial for continual learning. In this pap… (voir plus)er, we study the loss of plasticity in deep continual RL from the lens of churn: network output variability for out-of-batch data induced by mini-batch training. We demonstrate that (1) the loss of plasticity is accompanied by the exacerbation of churn due to the gradual rank decrease of the Neural Tangent Kernel (NTK) matrix; (2) reducing churn helps prevent rank collapse and adjusts the step size of regular RL gradients adaptively. Moreover, we introduce Continual Churn Approximated Reduction (C-CHAIN) and demonstrate it improves learning performance and outperforms baselines in a diverse range of continual learning environments on OpenAI Gym Control, ProcGen, DeepMind Control Suite, and MinAtar benchmarks.

2025-05-01

ICML.cc/2025/Conference (poster)

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

Jiashun Liu

Ling Pan

Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help i… (voir plus)mprove sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of"polluting"the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.

2025-05-01

ICML.cc/2025/Conference (poster)

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

walter Mayor

The use of parallel actors for data collection has been an effective technique used in reinforcement learning (RL) algorithms. The manner in… (voir plus) which data is collected in these algorithms, controlled via the number of parallel environments and the rollout length, induces a form of bias-variance trade-off; the number of training passes over the collected data, on the other hand, must strike a balance between sample efficiency and overfitting. We conduct an empirical analysis of these trade-offs on PPO, one of the most popular RL algorithms that uses parallel actors, and establish connections to network plasticity and, more generally, optimization stability. We examine its impact on network architectures, as well as the hyper-parameter sensitivity when scaling data. Our analyses indicate that larger dataset sizes can increase final performance across a variety of settings, and that scaling parallel environments is more effective than increasing rollout lengths. These findings highlight the critical role of data collection strategies in improving agent performance.

2025-05-01

ICML.cc/2025/Conference (poster)

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

Ghada Sokar

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.

2025-01-22

ICLR.cc/2025/Conference (spotlight)

Neuroplastic Expansion in Deep Reinforcement Learning

Jiashun Liu

Ling Pan

2025-01-22

ICLR.cc/2025/Conference (poster)

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training

Brian R. Bartoldson

Siddarth Venkatraman

James Diffenderfer

Moksh J. Jain

Tal Ben-Nun

Seanie Lee

Minsu Kim

Yoshua Bengio

Bhavya Kailkhura

2025-01-01

arXiv.org (prépublication)

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training

Brian R. Bartoldson

Siddarth Venkatraman

James Diffenderfer

Moksh J. Jain

Tal Ben-Nun

Seanie Lee

Minsu Kim

Yoshua Bengio

Bhavya Kailkhura

2025-01-01

arXiv.org (prépublication)

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

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

Mixture of Experts in a Mixture of RL settings

Timon Willi

Gintare Karolina Dziugaite

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

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Gintare Karolina Dziugaite

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)