Towards Tractable Optimism in Model-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2006.11911v2
• Date: Fri, 3 Dec 2021 21:16:42 GMT
• Title: Towards Tractable Optimism in Model-Based Reinforcement Learning
• Authors: Aldo Pacchiano and Philip J. Ball and Jack Parker-Holder and Krzysztof Choromanski and Stephen Roberts
• Abstract summary: To be successful, an optimistic RL algorithm must over-estimate the true value function (optimism) but not by so much that it is inaccurate (estimation error) We re-interpret these scalable optimistic model-based algorithms as solving a tractable noise augmented MDP. We show that if this error is reduced, optimistic model-based RL algorithms can match state-of-the-art performance in continuous control problems.
• Score: 37.51073590932658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL). To be successful, an optimistic RL algorithm must over-estimate the true value function (optimism) but not by so much that it is inaccurate (estimation error). In the tabular setting, many state-of-the-art methods produce the required optimism through approaches which are intractable when scaling to deep RL. We re-interpret these scalable optimistic model-based algorithms as solving a tractable noise augmented MDP. This formulation achieves a competitive regret bound: $\tilde{\mathcal{O}}( |\mathcal{S}|H\sqrt{|\mathcal{A}| T } )$ when augmenting using Gaussian noise, where $T$ is the total number of environment steps. We also explore how this trade-off changes in the deep RL setting, where we show empirically that estimation error is significantly more troublesome. However, we also show that if this error is reduced, optimistic model-based RL algorithms can match state-of-the-art performance in continuous control problems.

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