Implicit Posteriori Parameter Distribution Optimization in Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Efficient exploration is a key challenge in deep reinforcement learning (DRL).
• Bayesian inference with distributional representations can enhance agent exploration.
• Optimizing Bayesian neural networks (BNNs) often requires explicit parameter distributions, limiting flexibility.

Purpose of the Study:

• To propose a novel algorithm, implicit posteriori parameter distribution optimization (IPPDO), for improved sample efficiency and exploration in DRL.
• To address the limitations of explicit parameter distributions in BNN optimization.
• To enhance the representation of parameter uncertainty in learned policies.

Main Methods:

• Modeling parameters with implicit distributions approximated by generative models and learned latent spaces.
• Utilizing an energy-based model (EBM) with a value function to optimize implicit posteriori parameter distributions.
• Employing amortized Stein variational gradient descent (SVGD) for efficient model training.

Main Results:

• The proposed IPPDO algorithm implicitly represents parameter uncertainty in learned policies.
• Experiments on OpenAI Gym, MuJoCo, and Box2D platforms show IPPDO outperforms competing DRL algorithms.
• The method demonstrates improved sample efficiency and exploration capabilities.

Conclusions:

• IPPDO offers a flexible and effective approach to Bayesian deep reinforcement learning.
• The algorithm enhances exploration and performance by optimizing implicit parameter distributions.
• This work advances the state-of-the-art in intelligent agent exploration.