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Found 32 papers, 15 papers with code
Unlocking the Power of Representations in Long-term Novelty-based Exploration
We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space.
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
With simple linear algebra, we show that when using a linear predictor, the optimal predictor is close to an orthogonal projection, and propose a general framework based on orthonormalization that enables to interpret and give intuition on why BYOL works.
Understanding Self-Predictive Learning for Reinforcement Learning
We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse.
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains.
Emergent Communication at Scale
Emergent communication aims for a better understanding of human language evolution and building more efficient representations.
Geometric Entropic Exploration
Exploration is essential for solving complex Reinforcement Learning (RL) tasks.
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
BYOL works even without batch statistics
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation.
Bootstrap your own latent: A new approach to self-supervised Learning
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
Ranked #2 on
Self-Supervised Person Re-Identification
on SYSU-30k
Representation Learning
Self-Supervised Image Classification
+3
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
These latent embeddings are themselves trained to be predictive of the aforementioned representations.
Agent57: Outperforming the Atari Human Benchmark
Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade.
Ranked #1 on
Atari Games
on Atari 2600 HERO
Never Give Up: Learning Directed Exploration Strategies
Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344. 0%.
Ranked #7 on
Atari Games
on atari game
Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement learning.
Towards Consistent Performance on Atari using Expert Demonstrations
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Neural Predictive Belief Representations
In partially observable domains it is important for the representation to encode a belief state, a sufficient statistic of the observations seen so far.
Playing the Game of Universal Adversarial Perturbations
We study the problem of learning classifiers robust to universal adversarial perturbations.
Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent improvements to the DQN algorithm.
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards.
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration.
Ranked #1 on
Atari Games
on Atari 2600 Surround
Observational Learning by Reinforcement Learning
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent.
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting.
Ranked #6 on
Atari Games
on Atari 2600 Crazy Climber
Deep Q-learning from Demonstrations
We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.
End-to-end optimization of goal-driven and visually grounded dialogue systems
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.
Is the Bellman residual a bad proxy?
This paper aims at theoretically and empirically comparing two standard optimization criteria for Reinforcement Learning: i) maximization of the mean value and ii) minimization of the Bellman residual.
Difference of Convex Functions Programming Applied to Control with Expert Data
This paper reports applications of Difference of Convex functions (DC) programming to Learning from Demonstrations (LfD) and Reinforcement Learning (RL) with expert data.
Difference of Convex Functions Programming for Reinforcement Learning
Controlling this residual allows controlling the distance to the optimal action-value function, and we show that minimizing an empirical norm of the OBR is consistant in the Vapnik sense.
Inverse Reinforcement Learning through Structured Classification
This paper adresses the inverse reinforcement learning (IRL) problem, that is inferring a reward for which a demonstrated expert behavior is optimal.
