17 papers
. ~-learning (Watkins, 1989) is a simpleway for agentsto learn how to act optimallyin controlledMarkovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successivelyimprovingits evaluationsof the quali...
In reinforcement learning (RL), agents learn to act by trial and error, gradually improving their performance at the For many applications of reinforcement learn- task as learning progresses. Recent work in deep RL as- ing it can be more convenient to specify both sumes that agen...
reward Environment Learning Agent Reinforcement learning algorithms discover policies that observation maximize reward, but do not necessarily guarantee safety dur- actions ing learning or execution phases. We introduce a new ap- proach to learn optimal policies while enforcing p...
Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have to program the reward function...
Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learn- ing algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off...
However, there remain significant barriers to their deploy- ment in safety-critical engineering applications. Autonomous We propose a framework for verifiable and compositional vehicles, manufacturing robotics, and power systems manage- reinforcement learning (RL) in which a coll...
A common cardiac arrhythmia, known as heart block or AV block, is a condition where the electrical signal con- As the complexity of artificial pacemakers continues to grow, trolling the ventricular contractions are partially or com- the importance of capturing its functional corr...
. Research on multi-agent interaction involving both mul- tiple artificial agents and humans is still in its infancy. Most recent ap- proaches have focused on environments with collaboration-focused human behavior, or providing only a small, defined set of situations. When deploy...
by (default or sampled) safe ones; action projection, e.g., (Cheng et al. 2019), where unsafe actions are replaced by We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for safe reinforcement learning. projecting them to the safe action space; an...
Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Com- Multi-Agent Reinforcement Learning (MARL) has recently emerged plex Fully Cooperative Tasks. In Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Detroit, Mich...
at least some probability p. This framework comes from a probabilistic version of what is usually called the “safety” In real-life scenarios, a Reinforcement Learning (RL) agent aiming to maximise their reward, must often also behave in a fragment of Linear Temporal Logic (LTL) (...
not assume access to a simulator or environment abstraction, and Safety is a major concern in reinforcement learning (RL): we aim at is compatible with continuous environments. developing RL systems that not only perform optimally, but are also We also show that optimizing over s...