Search Results for author:
Found 12 papers, 5 papers with code
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning
$f$-MAX provides grounds for more directly comparing the objectives for LfD.
A Divergence Minimization Perspective on Imitation Learning Methods
We present $f$-MAX, an $f$-divergence generalization of AIRL [Fu et al., 2018], a state-of-the-art IRL method.
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
We examine the efficacy of our method on a variety of high-dimensional simulated continuous control tasks and observe that SMILe significantly outperforms Meta-BC.
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.
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
In this work, we closely investigate an important simplification of BCQ -- a prior approach for offline RL -- which removes a heuristic design choice and naturally restricts extracted policies to remain exactly within the support of a given behavior policy.
Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters.
Motivated by the success of MSG, we investigate whether efficient approximations to ensembles can be as effective.
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
While reward maximization is at the core of RL, reward engineering is not the only -- sometimes nor the easiest -- way for specifying complex behaviors.
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Despite the simplicity of this objective, the compositional nature of building diverse blueprints from a set of blocks leads to an explosion of complexity in structures that agents encounter.
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning
Most successes in robotic manipulation have been restricted to single-arm robots, which limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.
Ranked #17 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of pessimism for offline reinforcement learning (RL).
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
Without manually-designed controller nor human demonstrations, we demonstrate that with careful Sim2Real considerations, our policies trained with RL in simulation enable two xArm6 robots to solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning.
