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Found 16 papers, 13 papers with code
An Optimistic Perspective on Offline Deep Reinforcement Learning
The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.
On the Generalization of Representations in Reinforcement Learning
We complement our theoretical results with an empirical survey of classic representation learning methods from the literature and results on the Arcade Learning Environment, and find that the generalization behaviour of learned representations is well-explained by their effective dimension.
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
In this paper, we discuss how the implicit regularization effect of SGD seen in supervised learning could in fact be harmful in the offline deep RL setting, leading to poor generalization and degenerate feature representations.
Deep Reinforcement Learning at the Edge of the Statistical Precipice
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.
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
The shortcomings of maximum likelihood estimation in the context of model-based reinforcement learning have been highlighted by an increasing number of papers.
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states.
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network.
IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media.
Revisiting Fundamentals of Experience Replay
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Neural Additive Models: Interpretable Machine Learning with Neural Nets
They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.
Striving for Simplicity in Off-Policy Deep Reinforcement Learning
This paper advocates the use of offline (batch) reinforcement learning (RL) to help (1) isolate the contributions of exploitation vs. exploration in off-policy deep RL, (2) improve reproducibility of deep RL research, and (3) facilitate the design of simpler deep RL algorithms.
An Optimistic Perspective on Offline Reinforcement Learning
The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.
Learning to Generalize from Sparse and Underspecified Rewards
The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy.
Evaluation Function Approximation for Scrabble
The current state-of-the-art Scrabble agents are not learning-based but depend on truncated Monte Carlo simulations and the quality of such agents is contingent upon the time available for running the simulations.
