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View article: Generalization in Monitored Markov Decision Processes (Mon-MDPs)
Generalization in Monitored Markov Decision Processes (Mon-MDPs) Open
Reinforcement learning (RL) typically models the interaction between the agent and environment as a Markov decision process (MDP), where the rewards that guide the agent's behavior are always observable. However, in many real-world scenari…
View article: Monitored Markov Decision Processes
Monitored Markov Decision Processes Open
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assumption that rewards are always observable is often not appli…
View article: Learning to Be Cautious
Learning to Be Cautious Open
A key challenge in the field of reinforcement learning is to develop agents that behave cautiously in novel situations. It is generally impossible to anticipate all situations that an autonomous system may face or what behavior would best …
View article: Transfer Learning for Prosthetics Using Imitation Learning
Transfer Learning for Prosthetics Using Imitation Learning Open
In this paper, We Apply Reinforcement learning (RL) techniques to train a realistic biomechanical model to work with different people and on different walking environments. We benchmarking 3 RL algorithms: Deep Deterministic Policy Gradien…