Search Results for author:
Found 17 papers, 10 papers with code
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
This paper introduces a novel method for enhancing the effectiveness of on-policy Deep Reinforcement Learning (DRL) algorithms.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Composing Task Knowledge with Modular Successor Feature Approximators
Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework has been proposed as a method for learning, composing, and transferring predictive knowledge and behavior.
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
We use these tasks to benchmark well-established and state-of-the-art Bayesian deep learning methods on task-specific evaluation metrics.
In-context Reinforcement Learning with Algorithm Distillation
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.
Model-based Reinforcement Learning
Rolling Shutter Correction
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
This allows us to disentangle shared features and dynamics of the environment from agent-specific rewards and policies.
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions.
Uncertainty Evaluation Metric for Brain Tumour Segmentation
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification.
Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
From our comparison we conclude that some current techniques which solve benchmarks such as UCI `overfit' their uncertainty to the dataset---when evaluated on our benchmark these underperform in comparison to simpler baselines.
On the Importance of Opponent Modeling in Auction Markets
The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions.
Reinforcement Learning for Portfolio Management
In this thesis, we develop a comprehensive account of the expressive power, modelling efficiency, and performance advantages of so-called trading agents (i. e., Deep Soft Recurrent Q-Network (DSRQN) and Mixture of Score Machines (MSM)), based on both traditional system identification (model-based approach) as well as on context-independent agents (model-free approach).
Generalizing from a few environments in safety-critical reinforcement learning
Before deploying autonomous agents in the real world, we need to be confident they will perform safely in novel situations.
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Given the expert agent's demonstrations, we attempt to discover their strategy by modelling their latent reward function using linear and Gaussian process (GP) regressors from previous literature, and our own approach through Bayesian neural networks (BNN).
