Pablo Samuel Castro
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Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother
Joshua Greaves
Charline Le Lan
Marc Bellemare
International Conference on Learning Representations (ICLR)(2023)
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Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well-understood; 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.
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Offline Reinforcement Learning with On-Policy Q-Function Regularization
Laixi Shi
Yuejie Chi
Matthieu Geist
European Conference on Machine Learning (ECML)(2023)
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The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history dataset and the desired policy. A large portion of prior work tackles this challenge by implicitly/explicitly regularizing the learning policy towards the behavior policy, which
is hard to estimate reliably in practice. In this work, we propose to regularize towards the Q-function of the behavior policy instead of the behavior policy itself, under the premise that the Q-function can be estimated more reliably and easily by a SARSA-style estimate and handles the extrapolation error more straightforwardly. We propose two algorithms taking advantage of the estimated Q-function through regularizations, and demonstrate they exhibit strong performance on the D4RL benchmarks.
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Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning. We present a novel perspective on
behavioural metrics for Markov decision processes via the use of positive definite
kernels. We leverage this new perspective to define a new metric that is provably
equivalent to the recently introduced MICo distance (Castro et al., 2021). The kernel perspective further enables us to provide new theoretical results, which has so far
eluded prior work. These include bounding value function differences by means of
our metric, and the demonstration that our metric can be provably embedded into a
finite-dimensional Euclidean space with low distortion error. These are two crucial
properties when using behavioural metrics for reinforcement learning representations. We complement our theory with strong empirical results that demonstrate
the effectiveness of these methods in practice.
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Bigger, Better, Faster: Human-level Atari with human-level efficiency
Max Schwarzer
Johan Obando Ceron
Aaron Courville
Marc Bellemare
ICML(2023)
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We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling 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 moving the goalpost for sample-efficient RL research on the ALE.
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Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Max Allen Schwarzer
Aaron Courville
Marc G. Bellemare
NeurIPS(2022)
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Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo multiple design or algorithmic changes during their development cycle and use ad hoc approaches for incorporating these changes without re-training from scratch, which would have been prohibitively expensive. Additionally, the inefficiency of deep RL typically excludes researchers without access to industrial-scale resources from tackling computationally-demanding problems. To address these issues, we present reincarnating RL as an alternative workflow or class of problem settings, where prior computational work (e.g., learned policies) is reused or transferred between design iterations of an RL agent, or from one RL agent to another. As a step towards enabling reincarnating RL from any agent to any other agent, we focus on the specific setting of efficiently transferring an existing sub-optimal policy to a standalone value-based RL agent. We find that existing approaches fail in this setting and propose a simple algorithm to address their limitations. Equipped with this algorithm, we demonstrate reincarnating RL's gains over tabula rasa RL on Atari 2600 games, a challenging locomotion task, and the real-world problem of navigating stratospheric balloons. Overall, this work argues for an alternative approach to RL research, which we believe could significantly improve real-world RL adoption and help democratize it further. Open-sourced code and trained agents at
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A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani
Simone Totaro
Robert Müller
Shivam Garg
Matthieu Geist
Marlos C. Machado
Nicolas Le Roux
AISTATS(2022)
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Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong theoretical guarantees, leading to algorithms such as TRPO, PPO, or MPO. Rather than design yet another surrogate function, we instead propose a general framework (FMA-PG) based on functional mirror ascent that gives rise to an entire family of surrogate functions. We construct surrogate functions that enable policy improvement guarantees, a property not shared by most existing surrogate functions. Crucially, these guarantees hold regardless of the choice of policy parameterization. Moreover, a particular instantiation of FMA-PG recovers important implementation heuristics (e.g., using forward vs reverse KL divergence) resulting in a variant of TRPO with additional desirable properties. Via experiments on simple reinforcement learning problems, we evaluate the algorithms instantiated by FMA-PG. The proposed framework also suggests an improved variant of PPO, whose robustness and efficiency we empirically demonstrate on the MuJoCo suite.
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The State of Sparse Training in Deep Reinforcement Learning
Erich Elsen
Proceedings of the 39th International Conference on Machine Learning, PMLR(2022)
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The use of sparse neural networks has seen a rapid growth in recent years, particularly in computer vision; their appeal stems largely due to the reduced number of parameters required to train and store, as well as in an increase in learning efficiency. Somewhat surprisingly, there have been very few efforts exploring their use in deep reinforcement learning (DRL). In this work we perform a systematic investigation into applying a number of existing sparse training techniques on a variety of DRL agents and environments. Our results highlight the overall challenge that reinforcement learning poses for sparse training methods, complemented by detailed analyses on how the various components in DRL are affected by the use of sparse networks. We conclude by suggesting some promising avenues for improving the effectiveness of general sparse training methods, as well as for advancing their use in DRL.
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Deep Reinforcement Learning at the Edge of the Statistical Precipice
Max Schwarzer
Aaron Courville
Marc G. Bellemare
Advances in Neural Information Processing Systems(2021)
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Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs. Beginning with the Arcade Learning Environment (ALE), the shift towards computationally-demanding benchmarks has led to the practice of evaluating only a small number of runs per task, exacerbating the statistical uncertainty in point estimates. In this paper, we argue that reliable evaluation in the few run deep RL regime cannot ignore the uncertainty in results without running the risk of slowing down progress in the field. We illustrate this point using a case study on the Atari 100k benchmark, where we find substantial discrepancies between conclusions drawn from point estimates alone versus a more thorough statistical analysis. With the aim of increasing the field's confidence in reported results with a handful of runs, we advocate for reporting interval estimates of aggregate performance and propose performance profiles to account for the variability in results, as well as present more robust and efficient aggregate metrics, such as interquartile mean scores, to achieve small uncertainty in results. Using such statistical tools, we scrutinize performance evaluations of existing algorithms on other widely used RL benchmarks including the ALE, Procgen, and the DeepMind Control Suite, again revealing discrepancies in prior comparisons. Our findings call for a change in how we evaluate performance in deep RL, for which we present a more rigorous evaluation methodology, accompanied with an open-source library rliable, to prevent unreliable results from stagnating the field. See agarwl.github.io/rliable for more details.
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Revisiting Rainbow: More inclusive deep reinforcement learning research
Johan Samir Obando-Cerón
Proceedings of the 38th International Conference on Machine Learning, PMLR(2021)
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Since the introduction of DQN by \cite{mnih2015humanlevel}, a vast majority of reinforcement learning research has focused on reinforcement learning with the use of deep neural networks. New methods are typically evaluated on a set of standard environments that have now become standard, such as the Arcade Learning Environment (ALE) \citep{bellemare2012ale}. While these benchmarks help standardize evaluation, their computational cost has the unfortunate side effect of widening the gap between those with ample access to computational resources, and those without. In this work we argue that, despite the community's emphasis on large-scale environments, the traditional ``small-scale'' environments can still yield valuable scientific insights and can help reduce the barriers to entry for newcomers from underserved communities. To substantiate our claims, we empirically revisit paper which introduced the Rainbow algorithm \citep{hessel18rainbow} and present some new insights into the algorithms used by Rainbow.
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We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis, we provide empirical evidence that learning this distance alongside the value function yields structured and informative representations, including strong results on the Arcade Learning Environment benchmark.
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