MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, Carmelo Sferrazza
2024-12-17
Summary
This paper introduces MaxInfoRL, a new method in reinforcement learning that improves how robots and AI systems explore their environments by maximizing the information they gain from their actions.
What's the problem?
In reinforcement learning, algorithms need to find a balance between using known strategies that work well (exploitation) and trying out new actions that might lead to better rewards (exploration). Many existing methods use random actions to explore, which can be inefficient and may not lead to the best outcomes. Additionally, combining different types of rewards for tasks can be difficult and often depends on the specific task at hand.
What's the solution?
MaxInfoRL addresses these challenges by focusing exploration on actions that provide the most information about the environment. It uses intrinsic rewards—like curiosity—to guide the exploration process. By combining this with a method called Boltzmann exploration, MaxInfoRL helps systems make smarter choices about which actions to take, leading to better learning and performance. The method has been tested in various scenarios and has shown improved results compared to traditional approaches.
Why it matters?
This research is important because it enhances how AI systems learn to navigate and interact with complex environments. By making exploration more efficient, MaxInfoRL can help improve the performance of robots and AI in real-world applications, such as robotics, gaming, and autonomous vehicles. This could lead to smarter and more capable systems that can adapt better to new challenges.
Abstract
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. Exploration can also be directed using intrinsic rewards, such as curiosity or model epistemic uncertainty. However, effectively balancing task and intrinsic rewards is challenging and often task-dependent. In this work, we introduce a framework, MaxInfoRL, for balancing intrinsic and extrinsic exploration. MaxInfoRL steers exploration towards informative transitions, by maximizing intrinsic rewards such as the information gain about the underlying task. When combined with Boltzmann exploration, this approach naturally trades off maximization of the value function with that of the entropy over states, rewards, and actions. We show that our approach achieves sublinear regret in the simplified setting of multi-armed bandits. We then apply this general formulation to a variety of off-policy model-free RL methods for continuous state-action spaces, yielding novel algorithms that achieve superior performance across hard exploration problems and complex scenarios such as visual control tasks.