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ProSpec RL: Plan Ahead, then Execute (2407.21359v1)

Published 31 Jul 2024 in cs.LG, cs.AI, and cs.IR

Abstract: Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition. However, mainstream model-free Reinforcement Learning (RL) methods lack the ability to proactively envision future scenarios, plan, and guide strategies. These methods typically rely on trial and error to adjust policy functions, aiming to maximize cumulative rewards or long-term value, even if such high-reward decisions place the environment in extremely dangerous states. To address this, we propose the Prospective (ProSpec) RL method, which makes higher-value, lower-risk optimal decisions by imagining future n-stream trajectories. Specifically, ProSpec employs a dynamic model to predict future states (termed "imagined states") based on the current state and a series of sampled actions. Furthermore, we integrate the concept of Model Predictive Control and introduce a cycle consistency constraint that allows the agent to evaluate and select the optimal actions from these trajectories. Moreover, ProSpec employs cycle consistency to mitigate two fundamental issues in RL: augmenting state reversibility to avoid irreversible events (low risk) and augmenting actions to generate numerous virtual trajectories, thereby improving data efficiency. We validated the effectiveness of our method on the DMControl benchmarks, where our approach achieved significant performance improvements. Code will be open-sourced upon acceptance.

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References (47)
  1. Human-level control through deep reinforcement learning. nature, 518(7540):529–533, 2015.
  2. Mastering the game of go without human knowledge. nature, 550(7676):354–359, 2017.
  3. Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(7839):604–609, 2020.
  4. Dmbp: Diffusion model based predictor for robust offline reinforcement learning against state observation perturbations. In The Twelfth International Conference on Learning Representations, 2023.
  5. Temporal difference learning for model predictive control. arXiv preprint arXiv:2203.04955, 2022.
  6. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643, 2020.
  7. Improving sample efficiency in model-free reinforcement learning from images. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10674–10681, 2021.
  8. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47:253–279, 2013.
  9. Agent57: Outperforming the atari human benchmark. In International conference on machine learning, pages 507–517. PMLR, 2020.
  10. Task-aware world model learning with meta weighting via bi-level optimization. Advances in Neural Information Processing Systems, 36, 2024.
  11. Repo: Resilient model-based reinforcement learning by regularizing posterior predictability. Advances in Neural Information Processing Systems, 36, 2024.
  12. Image augmentation is all you need: Regularizing deep reinforcement learning from pixels. In International conference on learning representations, 2020.
  13. Reinforcement learning with augmented data. Advances in neural information processing systems, 33:19884–19895, 2020.
  14. Playvirtual: Augmenting cycle-consistent virtual trajectories for reinforcement learning. Advances in Neural Information Processing Systems, 34:5276–5289, 2021.
  15. Reinforcement learning with unsupervised auxiliary tasks. arXiv preprint arXiv:1611.05397, 2016.
  16. Data-efficient reinforcement learning with self-predictive representations. arXiv preprint arXiv:2007.05929, 2020.
  17. Value-consistent representation learning for data-efficient reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 11069–11077, 2023.
  18. When to trust your model: Model-based policy optimization. Advances in neural information processing systems, 32, 2019.
  19. Plan online, learn offline: Efficient learning and exploration via model-based control. arXiv preprint arXiv:1811.01848, 2018.
  20. The future of memory: remembering, imagining, and the brain. Neuron, 76(4):677–694, 2012.
  21. Lisa G Aspinwall. The psychology of future-oriented thinking: From achievement to proactive coping, adaptation, and aging. Motivation and emotion, 29:203–235, 2005.
  22. Peter McKiernan. Prospective thinking; scenario planning meets neuroscience. Technological Forecasting and Social Change, 124:66–76, 2017.
  23. On the nature of everyday prospection: A review and theoretical integration of research on mind-wandering, future thinking, and prospective memory. Review of General Psychology, 24(3):210–237, 2020.
  24. The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481):773–786, 2007.
  25. Deconstructing episodic memory with construction. Trends in cognitive sciences, 11(7):299–306, 2007.
  26. The evolution of foresight: What is mental time travel, and is it unique to humans? Behavioral and brain sciences, 30(3):299–313, 2007.
  27. Pilco: A model-based and data-efficient approach to policy search. In Proceedings of the 28th International Conference on machine learning (ICML-11), pages 465–472, 2011.
  28. Deep dyna-q: Integrating planning for task-completion dialogue policy learning. arXiv preprint arXiv:1801.06176, 2018.
  29. World models. arXiv preprint arXiv:1803.10122, 2018.
  30. Mopo: Model-based offline policy optimization. Advances in Neural Information Processing Systems, 33:14129–14142, 2020.
  31. When to use parametric models in reinforcement learning? Advances in Neural Information Processing Systems, 32, 2019.
  32. Kacper Piotr Kielak. Do recent advancements in model-based deep reinforcement learning really improve data efficiency? 2019.
  33. Curl: Contrastive unsupervised representations for reinforcement learning. In International conference on machine learning, pages 5639–5650. PMLR, 2020.
  34. Bootstrap latent-predictive representations for multitask reinforcement learning. In International Conference on Machine Learning, pages 3875–3886. PMLR, 2020.
  35. Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model. Advances in Neural Information Processing Systems, 33:741–752, 2020.
  36. Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193, 2020.
  37. Reinforcement learning: An introduction. MIT press, 2018.
  38. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016.
  39. Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
  40. Deepmind control suite. arXiv preprint arXiv:1801.00690, 2018.
  41. Forward-backward reinforcement learning. arXiv preprint arXiv:1803.10227, 2018.
  42. Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350–354, 2019.
  43. Dueling network architectures for deep reinforcement learning. In International conference on machine learning, pages 1995–2003. PMLR, 2016.
  44. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019.
  45. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271–21284, 2020.
  46. Learning latent dynamics for planning from pixels. In International conference on machine learning, pages 2555–2565. PMLR, 2019.
  47. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.

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