7 papers · 1 filter
Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
Balázs Gyenes, Emiliyan Gospodinov, Jan Frieling +5
High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale i…
PAWS: Preference Learning with Advantage-Weighted Segments
Aleksandar Taranovic, Onur Celik, Niklas Freymuth +6
Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demonstrations. Existing methods…
Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns
Dong Tian, Onur Celik, Gerhard Neumann
We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated -step targets. Unli…
DIME:Diffusion-Based Maximum Entropy Reinforcement Learning
Onur Celik, Zechu Li, Denis Blessing +5
Maximum entropy reinforcement learning (MaxEnt-RL) has become the standard approach to RL due to its beneficial exploration properties. Traditionally, policies are parameterized us…
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
Ge Li, Dong Tian, Hongyi Zhou +3
This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policie…
MaIL: Improving Imitation Learning with Mamba
Xiaogang Jia, Qian Wang, Atalay Donat +7
This work presents Mamba Imitation Learning (MaIL), a novel imitation learning (IL) architecture that provides an alternative to state-of-the-art (SoTA) Transformer-based policies.…