EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
-cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC…
Arxiv 2 versions
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5 new sentences, beginning: “EfficientTDMPC introduces three contributions that improve performance by aiming to reduce this error.”
EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
-cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is a planner that aims to find an action sequence that maximizes the estimated return. The return is estimated using a learned model and value networks, each of which can introduce error. EfficientTDMPC introduces three contributions that improve performance by aiming to reduce this error. First, we introduce an aggregate multi-horizon planning objective that evaluates the value at different rollout depths and averages them. Second, we introduce ensembles for state-action value estimation to value-equivalent/MuZero-style model-based RL methods. Third, we add pessimistic reanalyze, which penalizes uncertain return estimates when creating policy targets. We evaluate EfficientTDMPC on HumanoidBench and the DeepMind Control Suite, to the best of our knowledge, it is the new state of the art on both domains in terms of sample efficiency.
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EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
-cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is a planner that aims to find an action sequence that maximizes the estimated return. The return is estimated using a learned model and value networks, each of which can introduce error. EfficientTDMPC proposes to reduce this error in two ways. First, it introduces an ensemble of dynamics models and averages the return estimates across those models and across different rollout depths. Second, it adds the option to apply an uncertainty penalty to the planner objective, yielding a planner that avoids actions with uncertain return estimates. It then adds practical improvements which increase buffer data freshness and reduce compute. Lastly, we find that our contributions enable EfficientTDMPC to benefit more from a higher update-to-data (UTD) ratio, further improving sample efficiency. To the best of our knowledge, in the low data regime of each benchmark, EfficientTDMPC achieves state-of-the-art (SOTA) in terms of sample efficiency on HumanoidBench-Hard and DMC hard, while matching SOTA on DMC easy.
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| Factor | Weight | Score | Contribution | Where it came from |
|---|---|---|---|---|
| Corroboration | 0.35 | 0.39 | +0.135 26% | 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true. |
| Source trustleads | 0.25 | 0.85 | +0.212 41% | arXiv is the highest-trust source on this story and is first-party — the organisation announcing its own news. Trust is taken from the best source, not averaged. |
| Pickup rate | 0.20 | 0.00 | +0.000 0% | One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate. |
| Freshness | 0.20 | 0.85 | +0.171 33% | Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent. |
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What happened
-cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is a planner that aims to find an action sequence that maximizes the estimated return. The return is estimated using a learned model and value networks, each of which can introduce error. EfficientTDMPC introduces three contributions that improve performance by aiming to reduce this error. First, we introduce an aggregate multi-horizon planning objective that evaluates the value at different rollout depths and averages them. Second, we introduce ensembles for state-action value estimation to value-equivalent/MuZero-style model-based RL methods.
How this story arrived
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