Search Results for author:
Found 14 papers, 5 papers with code
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
We address the long-horizon mapless navigation problem: enabling robots to traverse novel environments without relying on high-definition maps or precise waypoints that specify exactly where to navigate.
RL Zero: Zero-Shot Language to Behaviors without any Supervision
In this work, we show that we can achieve a zero-shot language-to-behavior policy by first grounding the imagined sequences in real observations of an unsupervised RL agent and using a closed-form solution to imitation learning that allows the RL agent to mimic the grounded observations.
Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning
We present \emph{Proto Successor Measure}: the basis set for all possible solutions of Reinforcement Learning in a dynamical system.
A Dual Approach to Imitation Learning from Observations with Offline Datasets
DILO reduces the learning from observations problem to that of simply learning an actor and a critic, bearing similar complexity to vanilla offline RL.
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Reinforcement Learning from Human Feedback (RLHF) has been crucial to the recent success of Large Language Models (LLMs), however, it is often a complex and brittle process.
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail.
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning
Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward functions.
Contrastive Preference Learning: Learning from Human Feedback without RL
Thus, learning a reward function from feedback is not only based on a flawed assumption of human preference, but also leads to unwieldy optimization challenges that stem from policy gradients or bootstrapping in the RL phase.
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
For offline RL, our analysis frames a recent offline RL method XQL in the dual framework, and we further propose a new method f-DVL that provides alternative choices to the Gumbel regression loss that fixes the known training instability issue of XQL.
A Ranking Game for Imitation Learning
We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward.
Lyapunov Barrier Policy Optimization
Current RL agents explore the environment without considering these constraints, which can lead to damage to the hardware or even other agents in the environment.
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Our method outperforms adversarial imitation learning methods in terms of sample efficiency and the required number of expert trajectories on IRL benchmarks.
Learning Off-Policy with Online Planning
In this work, we investigate a novel instantiation of H-step lookahead with a learned model and a terminal value function learned by a model-free off-policy algorithm, named Learning Off-Policy with Online Planning (LOOP).
Imitative Planning using Conditional Normalizing Flow
A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampling in the trajectory space to find the minimum cost trajectory.
