Search Results for author:
Found 10 papers, 3 papers with code
Task-conditioned adaptation of visual features in multi-task policy learning
We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that, compared to existing work, it can be addressed with a single policy.
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning
In this paper, we present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using RL.
AutoNeRF: Training Implicit Scene Representations with Autonomous Agents
Empirical results show that NeRFs can be trained on actively collected data using just a single episode of experience in an unseen environment, and can be used for several downstream robotic tasks, and that modular trained exploration models outperform other classical and end-to-end baselines.
Multi-Object Navigation with dynamically learned neural implicit representations
Understanding and mapping a new environment are core abilities of any autonomously navigating agent.
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL).
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals.
DisTop: Discovering a Topological representation to learn diverse and rewarding skills
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
ELSIM: End-to-end learning of reusable skills through intrinsic motivation
Then we show that our approach can scale on more difficult MuJoCo environments in which our agent is able to build a representation of skills which improve over a baseline both transfer learning and exploration when rewards are sparse.
A survey on intrinsic motivation in reinforcement learning
In this article, we provide a survey on the role of intrinsic motivation in DRL.
Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning
An important issue in Reinforcement Learning (RL) is to accelerate or improve the learning process.
