7 papers
Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions
Matthew Riemer, Tommaso Tosato, Amin Memarian +4
This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral cer…
Task Robustness via Re-Labelling Vision-Action Robot Data
Artur Kuramshin, Ãzgür Aslan, Cyrus Neary +1
The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios. However, th…
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
Faisal Mohamed, Catherine Ji, Benjamin Eysenbach +1
Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent perceives and represents the world. To lear…
Align and Filter: Improving Performance in Asynchronous On-Policy RL
Homayoun Honari, Roger Creus Castanyer, Michael Przystupa +3
Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, but both exacerbate a central challenge:…
Is Exploration or Optimization the Problem for Deep Reinforcement Learning?
Glen Berseth
In the era of deep reinforcement learning, making progress is more complex, as the collected experience must be compressed into a deep model for future exploitation and sampling. M…
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother +3
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversaria…