REVIEW 6 cited by
Mirror Descent Policy Optimization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice. Inspired by this, we propose an efficient RL algorithm, called {\em mirror descent policy optimization} (MDPO). MDPO iteratively updates the policy by {\em approximately} solving a trust-region problem, whose objective function consists of two terms: a linearization of the standard RL objective and a proximity term that restricts two consecutive policies to be close to each other. Each update performs this approximation by taking multiple gradient steps on this objective function. We derive {\em on-policy} and {\em off-policy} variants of MDPO, while emphasizing important design choices motivated by the existing theory of MD in RL. We highlight the connections between on-policy MDPO and two popular trust-region RL algorithms: TRPO and PPO, and show that explicitly enforcing the trust-region constraint is in fact {\em not} a necessity for high performance gains in TRPO. We then show how the popular soft actor-critic (SAC) algorithm can be derived by slight modifications of off-policy MDPO. Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous control tasks. Code is available at \url{https://github.com/manantomar/Mirror-Descent-Policy-Optimization}.
Forward citations
Cited by 6 Pith papers
-
On the Policy Convergence of Policy Mirror Descent Methods
Unregularized PMD with any constant step size converges to a limiting optimal policy for general decomposable Legendre mirror maps, with behavior governed by differentiability of ψ at 0 and 1.
-
StaQ it! Growing neural networks for Policy Mirror Descent
StaQ, a finite-memory Policy Mirror Descent algorithm, converges to the optimal entropy-regularized policy with a sufficiently large window of past Q-functions and performs competitively with baselines.
-
On the Effect of Regularization in Policy Mirror Descent
A large empirical sweep shows that in Policy Mirror Descent, MDP and Drift regularizers are partly substitutable, yet their precise combination determines temperature robustness.
-
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
Under variational gradient dominance, the paper derives state-space-independent sample complexity bounds for SDPO, CPI, DA-CPI, and PMD in agnostic policy learning.
-
Efficient Online Reinforcement Learning for Diffusion Policy
Diffusion policies can be trained online with reweighted score matching using only Q-functions, and the resulting DPMD and SDAC algorithms beat SAC and prior diffusion-policy RL on most MuJoCo tasks.
-
A Single Diffusion-Policy Controller for Multi-Task Block Pushing with Zero-Shot Sim-to-Real Transfer
One diffusion policy trained via energy-guided RL solves multi-shape block pushing without demos and transfers zero-shot to real robots under varied conditions.
Discussion (0). Continue with ORCID to comment.