Search Results for author:
Found 15 papers, 3 papers with code
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not collected on the actual deployment environment but on a different version.
Learning Energy Networks with Generalized Fenchel-Young Losses
To learn the parameters of the energy function, the solution to that optimization problem is typically fed into a loss function.
Continuous Control with Action Quantization from Demonstrations
The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations.
Offline Reinforcement Learning as Anti-Exploration
This is the converse of exploration in RL, which favors such actions.
What Matters for Adversarial Imitation Learning?
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
Offline Reinforcement Learning with Pseudometric Learning
In the presence of function approximation, and under the assumption of limited coverage of the state-action space of the environment, it is necessary to enforce the policy to visit state-action pairs close to the support of logged transitions.
Show me the Way: Intrinsic Motivation from Demonstrations
Using an inverse RL approach, we show that complex exploration behaviors, reflecting different motivations, can be learnt and efficiently used by RL agents to solve tasks for which exhaustive exploration is prohibitive.
Primal Wasserstein Imitation Learning
Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert.
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
To test our hypothesis empirically, we augmented a standard deep RL agent with an auxiliary task of learning the value-improvement path.
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.
Statistics and Samples in Distributional Reinforcement Learning
We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distribution.
Distributional Reinforcement Learning
reinforcement-learning
+1
The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes.
A Geometric Perspective on Optimal Representations for Reinforcement Learning
We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks.
Boosting Model Performance through Differentially Private Model Aggregation
In this work, two approaches for private model aggregation are proposed that enable the transfer of knowledge from existing models trained on other companies' datasets to a new company with limited labeled data while protecting each client company's underlying individual sensitive information.
