560 citations · 3.3k across the 60 of their papers we have counts for
34 papers · 1 filter
Showing versus Doing: Teaching by Demonstration
Mark K. Ho, Michael L. Littman, James MacGlashan +2
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior
Zhiyuan Zhou, Shreyas Sundara Raman, Henry Sowerby +1
Reinforcement-learning agents seek to maximize a reward signal through environmental interactions. As humans, our job in the learning process is to design reward functions to expre…
Reward-Predictive Clustering
Lucas Lehnert, Michael J. Frank, Michael L. Littman
Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating…
Designing Rewards for Fast Learning
Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in…
Faster Deep Reinforcement Learning with Slower Online Network
Kavosh Asadi, Rasool Fakoor, Omer Gottesman +3
Deep reinforcement learning algorithms often use two networks for value function optimization: an online network, and a target network that tracks the online network with some dela…
On the Expressivity of Markov Reward
David Abel, Will Dabney, Anna Harutyunyan +4
Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way to capture tasks that we would want an a…
Bad-Policy Density: A Measure of Reinforcement Learning Hardness
David Abel, Cameron Allen, Dilip Arumugam +3
Reinforcement learning is hard in general. Yet, in many specific environments, learning is easy. What makes learning easy in one environment, but difficult in another? We address t…
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
Ishaan Shah, David Halpern, Kavosh Asadi +1
Fluid human-agent communication is essential for the future of human-in-the-loop reinforcement learning. An agent must respond appropriately to feedback from its human trainer even…
Model Selection's Disparate Impact in Real-World Deep Learning Applications
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey +2
Algorithmic fairness has emphasized the role of biased data in automated decision outcomes. Recently, there has been a shift in attention to sources of bias that implicate fairness…
Learning State Abstractions for Transfer in Continuous Control
Kavosh Asadi, David Abel, Michael L. Littman
Can simple algorithms with a good representation solve challenging reinforcement learning problems? In this work, we answer this question in the affirmative, where we take "simple…
Deep Radial-Basis Value Functions for Continuous Control
Kavosh Asadi, Neev Parikh, Ronald E. Parr +2
A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned…
Lipschitz Lifelong Reinforcement Learning
Erwan Lecarpentier, David Abel, Kavosh Asadi +3
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (…
Combating the Compounding-Error Problem with a Multi-step Model
Kavosh Asadi, Dipendra Misra, Seungchan Kim +1
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve lear…
Teaching with IMPACT
Carl Trimbach, Michael Littman
Like many problems in AI in their general form, supervised learning is computationally intractable. We hypothesize that an important reason humans can learn highly complex and vari…
Deep Reinforcement Learning from Policy-Dependent Human Feedback
Dilip Arumugam, Jun Ki Lee, Sophie Saskin +1
To widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users. Moreover, they will ne…
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback
Jacob Beck, Zoe Papakipos, Michael Littman
In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous and costly in the real world. For this reason we investigate methods of training an AV without allowing…
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
Lucas Lehnert, Michael L. Littman
A key question in reinforcement learning is how an intelligent agent can generalize knowledge across different inputs. By generalizing across different inputs, information learned…
Measuring and Characterizing Generalization in Deep Reinforcement Learning
Sam Witty, Jun Ki Lee, Emma Tosch +3
Deep reinforcement-learning methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has…
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
Kavosh Asadi, Evan Cater, Dipendra Misra +1
When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn…
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
Kavosh Asadi, Evan Cater, Dipendra Misra +1
Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model…
Transfer with Model Features in Reinforcement Learning
Lucas Lehnert, Michael L. Littman
A key question in Reinforcement Learning is which representation an agent can learn to efficiently reuse knowledge between different tasks. Recently the Successor Representation wa…
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi, Dipendra Misra, Michael L. Littman
We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipsc…