4 papers
Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
Haoxiang You, Yilang Liu, Davis Zong +5
We present the stochastic decoupled policy gradient (SDPG), a lightweight visual reinforcement learning (RL) method that trains diverse visuomotor control policies end-to-end withi…
Sample-Based Hybrid Mode Control: Asymptotically Optimal Switching of Algorithmic and Non-Differentiable Control Modes
Yilang Liu, Haoxiang You, Ian Abraham
This paper investigates a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes. Our approach reasons about a set of hybri…
Accelerating Visual-Policy Learning through Parallel Differentiable Simulation
Haoxiang You, Yilang Liu, Ian Abraham
In this work, we propose a computationally efficient algorithm for visual policy learning that leverages differentiable simulation and first-order analytical policy gradients. Our…
Is Bellman Equation Enough for Learning Control?
Haoxiang You, Lekan Molu, Ian Abraham
The Bellman equation and its continuous-time counterpart, the Hamilton-Jacobi-Bellman (HJB) equation, serve as necessary conditions for optimality in reinforcement learning and opt…