Core Concepts
The authors explore continuous MDP homomorphisms and derive policy gradient theorems for stochastic and deterministic policies, enhancing policy optimization through state-action abstraction.
Abstract
The study delves into reinforcement learning on high-dimensional observations, emphasizing representation learning through MDP homomorphisms. It extends the concept to continuous settings, proving optimal value equivalence and deriving homomorphic policy gradient theorems. The research showcases the effectiveness of leveraging symmetries for improved sample efficiency in policy optimization.
Key points:
- Reinforcement learning relies on abstraction for efficient problem-solving.
- Bisimulation metrics are used for model minimization.
- MDP homomorphisms preserve value functions between MDPs.
- Continuous MDP homomorphisms extend to control dynamical systems.
- Homomorphic policy gradient theorems optimize policies using approximate symmetries.
Stats
R(s,a) = R(f(s),gs(a)) for every s ∈ S,a ∈ A;
τ gs(a)(f(s′)∣f(s)) = ∑s′′∈[s′]Bh∣S τa(s′′∣s);
Qπ↑(s,a) = Qπ(f(s),gs(a));
ρπθ(s) = limt→∞ γtP(st = s∣s0,a0∶t ∼ πθ);
Quotes
"Our method’s ability to utilize MDP homomorphisms for representation learning leads to improved performance."
"Continuous MDP homomorphisms extend to control dynamical systems in physical spaces."
"The study showcases leveraging approximate symmetries for improved sample efficiency."