exploration-strategies

What problem does it solve?

This Skill provides a structured framework to design, compare, and tune exploration strategies for deep reinforcement learning agents, helping them avoid local optima and efficiently discover sparse rewards.

Core Features & Use Cases

• Strategy comparison across ε-greedy, Boltzmann, and UCB, including their temperature or decay schedules.
• Intrinsic motivation integration with curiosity-driven methods and RND to sustain exploration in challenging environments.
• Practical guidance on balancing intrinsic and extrinsic rewards, and on parameter tuning for stable learning.
• Use Case: Apply these methods to a maze-like or sparse-reward task to improve sample efficiency and policy robustness.

Quick Start

1. Select an environment (e.g., gridworld or simple Atari-like task).
2. Choose an exploration strategy (ε-greedy with linear or exponential decay, Boltzmann with temperature, or UCB) and set basic hyperparameters.
3. Run a short training loop to observe exploration behavior and reward progression.