8 papers
SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions
Elham Daneshmand, Majid Khadiv, Glen Berseth +1
Training reinforcement-learning agents directly on physical robots makes every fall costly, since a fall can damage the platform and cannot be undone like a simulator reset; the go…
Agentick: A Unified Benchmark for General Sequential Decision-Making Agents
Roger Creus Castanyer, Pablo Samuel Castro, Glen Berseth
AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no unified benchmark enables fai…
SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion
Elham Daneshmand, Shafeef Omar, Glen Berseth +2
Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is…
ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning
Roger Creus Castanyer, Faisal Mohamed, Pablo Samuel Castro +2
Reinforcement learning (RL) algorithms are highly sensitive to reward function specification, which remains a central challenge limiting their broad applicability. We present ARM-F…
Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning
Roger Creus Castanyer, Johan Obando-Ceron, Lu Li +4
Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure mode remain poorly understood. Several re…
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
Hongyao Tang, Johan Obando-Ceron, Pablo Samuel Castro +2
Plasticity, or the ability of an agent to adapt to new tasks, environments, or distributions, is crucial for continual learning. In this paper, we study the loss of plasticity in d…