4 papers
A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets
Akihiro Kubo, Kosuke Nakanishi, Shin Ishii
Preference-conditioned multi-objective reinforcement learning aims to learn a single policy that captures trade-offs across preferences, but under nonlinear scalarization the uniqu…
Double Horizon Model-Based Policy Optimization
Akihiro Kubo, Paavo Parmas, Shin Ishii
Model-based reinforcement learning (MBRL) reduces the cost of real-environment sampling by generating synthetic trajectories (called rollouts) from a learned dynamics model. Howeve…
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation
Kosuke Nakanishi, Akihiro Kubo, Yuji Yasui +1
Recently, robust reinforcement learning (RL) methods designed to handle adversarial input observations have received significant attention, motivated by RL's inherent vulnerabiliti…
Robust off-policy Reinforcement Learning via Soft Constrained Adversary
Kosuke Nakanishi, Akihiro Kubo, Yuji Yasui +1
Recently, robust reinforcement learning (RL) methods against input observation have garnered significant attention and undergone rapid evolution due to RL's potential vulnerability…