GitHub

Policy gradient methods are a type of reinforcement learning techniques that rely upon ...

Policy Based agents directly learn a policy (a probability distribution of actions) mapping input states to output actions. • Effective in high-dimensional or continuous action spaces Policy gradient ...
Microsoft

Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms

In Reinforcement Learning, the optimal action at a given state is dependent on policy decisions at subsequent states. As a consequence, the learning targets evolve with time and the policy ...
Microsoft

Towards a generalized policy iteration theorem

Policy Iteration consists in a loop over two processing steps: policy evaluation and policy improvement. Policy Iteration has strong convergence properties when the policy evaluation is exact and the ...
IEEE

Distributed Gradient Descent: Nonconvergence to Saddle Points and the Stable-Manifold Theorem

Abstract: The paper studies continuous-time distributed gradient descent (DGD) and considers the problem of showing that in nonconvex optimization problems, DGD typically converges to local minima ...