VASE: Variational Assorted Surprise Exploration for Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Exploration in continuous control environments with sparse rewards is a significant challenge in reinforcement learning (RL).
• Intrinsic motivation, particularly surprise, is a promising approach to guide agent exploration.
• Existing surprise-based methods face limitations in complex, high-dimensional environments.

Purpose of the Study:

• To introduce a novel definition and implementation of surprise for efficient exploration in RL.
• To present Variational Assorted Surprise Exploration (VASE), an RL algorithm designed for sparse reward settings.
• To evaluate VASE's performance against other surprise-based exploration techniques.

Main Methods:

• Developed a new definition of surprise tailored for RL agents.
• Implemented VASE using a Bayesian neural network to model environment dynamics.
• Employed variational inference for alternating updates of the agent's environment model and policy.
• Conducted experiments in continuous control environments with sparse rewards.

Main Results:

• VASE demonstrated superior performance compared to existing surprise-based exploration methods.
• The proposed method effectively improved exploration efficiency in challenging environments.
• Bayesian neural networks and variational inference proved effective for modeling dynamics and guiding exploration.

Conclusions:

• VASE offers a significant advancement in RL exploration strategies, particularly for sparse reward problems.
• The approach provides a more systematic and efficient way for agents to learn in complex environments.
• This work opens new avenues for intrinsic motivation in artificial intelligence.