MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918108277506048 |
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| author | Sukhija, Bhavya Coros, Stelian Krause, Andreas Abbeel, Pieter Sferrazza, Carmelo |
| author_facet | Sukhija, Bhavya Coros, Stelian Krause, Andreas Abbeel, Pieter Sferrazza, Carmelo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12098 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization Sukhija, Bhavya Coros, Stelian Krause, Andreas Abbeel, Pieter Sferrazza, Carmelo Machine Learning Artificial Intelligence Robotics 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. |
| title | MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2412.12098 |