Raphaël Marinier
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View article: Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision
Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision Open
We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision. By combining two types of discrete speech representations, we cast TTS as a composition of two sequence-to-sequence tasks: fr…
View article: Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision
Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision Open
We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision. By combining two types of discrete speech representations, we cast TTS as a composition of two sequence-to-sequence tasks: fr…
View article: AudioLM: a Language Modeling Approach to Audio Generation
AudioLM: a Language Modeling Approach to Audio Generation Open
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation spa…
View article: Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO Open
Recent advances in multiagent learning have seen the introduction ofa family of algorithms that revolve around the population-based trainingmethod PSRO, showing convergence to Nash, correlated and coarse corre-lated equilibria. Notably, wh…
View article: RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning Open
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from D…
View article: Solving N-player dynamic routing games with congestion: a mean field\n approach
Solving N-player dynamic routing games with congestion: a mean field\n approach Open
The recent emergence of navigational tools has changed traffic patterns and\nhas now enabled new types of congestion-aware routing control like dynamic road\npricing. Using the fundamental diagram of traffic flows - applied in\nmacroscopic…
View article: Solving N-player dynamic routing games with congestion: a mean field approach
Solving N-player dynamic routing games with congestion: a mean field approach Open
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the fundamental diagram of traffic flows - applied in macroscopic an…
View article: RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning.
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning. Open
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from D…
View article: Hyperparameter Selection for Imitation Learning
Hyperparameter Selection for Imitation Learning Open
We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms in the context of continuous-control, when the underlying reward function of the demonstrating expert cannot be observed at any time. The vast literatur…
View article: Self-Attentional Credit Assignment for Transfer in Reinforcement Learning
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning Open
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents. Despite the apparent promises, transfer in RL is still an open and little exploited research area. In this paper, we ta…
View article: What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study Open
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- a…
View article: SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference Open
We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we show that it is not only possible to train on millions of frames per second but also to l…
View article: SEED RL: Scalable and Efficient Deep-RL with Accelerated Central\n Inference
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central\n Inference Open
We present a modern scalable reinforcement learning agent called SEED\n(Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we\nshow that it is not only possible to train on millions of frames per second but\nalso t…
View article: Credit Assignment as a Proxy for Transfer in Reinforcement Learning.
Credit Assignment as a Proxy for Transfer in Reinforcement Learning. Open
The ability to transfer representations to novel environments and tasks is a sensible requirement for general learning agents. Despite the apparent promises, transfer in Reinforcement Learning is still an open and under-exploited research …
View article: Self-Attentional Credit Assignment for Transfer in Reinforcement Learning
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning Open
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents. Despite the apparent promises, transfer in RL is still an open and little exploited research area. In this paper, we ta…
View article: Towards Accurate Generative Models of Video: A New Metric & Challenges
Towards Accurate Generative Models of Video: A New Metric & Challenges Open
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider v…
View article: Towards Accurate Generative Models of Video: A New Metric & Challenges
Towards Accurate Generative Models of Video: A New Metric & Challenges Open
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider v…
View article: Episodic Curiosity through Reachability
Episodic Curiosity through Reachability Open
Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more su…
View article: Episodic Curiosity through Reachability
Episodic Curiosity through Reachability Open
Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more su…