13 citations · 32 across the 7 of their papers we have counts for
9 papers
Shaping Advice in Deep Reinforcement Learning
Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran
Reinforcement learning involves agents interacting with an environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feed…
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning
Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran
This paper considers multi-agent reinforcement learning (MARL) tasks where agents receive a shared global reward at the end of an episode. The delayed nature of this reward affects…
Shaping Advice in Deep Multi-Agent Reinforcement Learning
Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran
Multi-agent reinforcement learning involves multiple agents interacting with each other and a shared environment to complete tasks. When rewards provided by the environment are spa…
Safety-Critical Online Control with Adversarial Disturbances
Bhaskar Ramasubramanian, Baicen Xiao, Linda Bushnell +1
This paper studies the control of safety-critical dynamical systems in the presence of adversarial disturbances. We seek to synthesize state-feedback controllers to minimize a cost…
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback
Baicen Xiao, Qifan Lu, Bhaskar Ramasubramanian +3
Reinforcement learning has been successful in training autonomous agents to accomplish goals in complex environments. Although this has been adapted to multiple settings, including…
Potential-Based Advice for Stochastic Policy Learning
Baicen Xiao, Bhaskar Ramasubramanian, Andrew Clark +3
This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies. We show…