Search Results for author:
Found 44 papers, 7 papers with code
Mastering Board Games by External and Internal Planning with Language Models
While large language models perform well on a range of complex tasks (e. g., text generation, question answering, summarization), robust multi-step planning and reasoning remains a considerable challenge for them.
APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT
This problem is formulated as mutual training of skills using an intrinsic reward and a discriminator trained to predict a skill given its trajectory.
Diversifying AI: Towards Creative Chess with AlphaZero
In particular, we investigate whether a team of diverse AI systems can outperform a single AI in challenging tasks by generating more ideas as a group and then selecting the best ones.
Acceleration in Policy Optimization
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates.
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization
Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution.
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Such applications often require to put constraints on the agent's behavior.
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task.
Discovering Evolution Strategies via Meta-Black-Box Optimization
Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies.
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
In this work, we aim to bring the best of both worlds and propose an algorithm that exhibits an exploratory behavior whilst it utilizes large diverse datasets.
Meta-Gradients in Non-Stationary Environments
We support these results with a qualitative analysis of resulting meta-parameter schedules and learned functions of context features.
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations.
Bootstrapped Meta-Learning
We achieve a new state-of-the art for model-free agents on the Atari ALE benchmark and demonstrate that it yields both performance and efficiency gains in multi-task meta-learning.
Emphatic Algorithms for Deep Reinforcement Learning
In this paper, we extend the use of emphatic methods to deep reinforcement learning agents.
Reward is enough for convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i. e., defined over state-action pairs and independent of time, is sufficient to capture many kinds of goals in a Markov decision process (MDP).
Discovering Diverse Nearly Optimal Policies with Successor Features
We propose Diverse Successive Policies, a method for discovering policies that are diverse in the space of Successor Features, while assuring that they are near optimal.
Online Apprenticeship Learning
Finally, we implement a deep variant of our algorithm which shares some similarities to GAIL \cite{ho2016generative}, but where the discriminator is replaced with the costs learned by the OAL problem.
Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.
Discovering a set of policies for the worst case reward
Our main contribution is a policy iteration algorithm that builds a set of policies in order to maximize the worst-case performance of the resulting SMP on the set of tasks.
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
To alleviate this, we propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online.
Online Limited Memory Neural-Linear Bandits
We study neural-linear bandits for solving problems where both exploration and representation learning play an important role.
Balancing Constraints and Rewards with Meta-Gradient D4PG
Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints.
Learning to Ask Medical Questions using Reinforcement Learning
We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection.
A Self-Tuning Actor-Critic Algorithm
Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters, typically requiring significant manual effort to identify hyperparameters that perform well on a new domain.
Deep Randomized Least Squares Value Iteration
Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by a DQN agent.
Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot
We propose a simple all-in-line single-shot scheme for diagnostics of ultrashort laser pulses, consisting of a multi-mode fiber, a nonlinear crystal and a CCD camera.
Apprenticeship Learning via Frank-Wolfe
Specifically, we show that a variation of the FW method that is based on taking "away steps" achieves a linear rate of convergence when applied to AL and that a stochastic version of the FW algorithm can be used to avoid precise estimation of feature expectations.
Contextual Inverse Reinforcement Learning
In this setting, the reward, which is unknown to the agent, is a function of a static parameter referred to as the context.
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching
We study neural-linear bandits for solving problems where both exploration and representation learning play an important role.
Unknown mixing times in apprenticeship and reinforcement learning
We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria.
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces.
Inverse Reinforcement Learning in Contextual MDPs
Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts).
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies
We consider a settings of hierarchical reinforcement learning, in which the reward is a sum of components.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching
We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information.
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant.
Deep Learning Reconstruction of Ultra-Short Pulses
Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create.
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies
In this work, we provide theoretical guarantees for reward decomposition in deterministic MDPs.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
Train on Validation: Squeezing the Data Lemon
We define the notion of on-average-validation-stable algorithms as one in which using small portions of validation data for training does not overfit the model selection process.
Shallow Updates for Deep Reinforcement Learning
In this work we propose a hybrid approach -- the Least Squares Deep Q-Network (LS-DQN), which combines rich feature representations learned by a DRL algorithm with the stability of a linear least squares method.
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce
Classifying products into categories precisely and efficiently is a major challenge in modern e-commerce.
Visualizing Dynamics: from t-SNE to SEMI-MDPs
Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots.
Deep Reinforcement Learning Discovers Internal Models
Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in challenging problems such as playing Atari, solving Go and controlling robots.
A Deep Hierarchical Approach to Lifelong Learning in Minecraft
Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network.
Graying the black box: Understanding DQNs
In recent years there is a growing interest in using deep representations for reinforcement learning.
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
As most deep learning algorithms are stochastic (e. g., Stochastic Gradient Descent, Dropout, and Bayes-by-backprop), we revisit the robustness arguments of Xu & Mannor, and introduce a new approach, ensemble robustness, that concerns the robustness of a population of hypotheses.
