Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings (2402.17135v1)

Published 27 Feb 2024 in cs.LG and cs.AI

Abstract: Can we pre-train a generalist agent from a large amount of unlabeled offline trajectories such that it can be immediately adapted to any new downstream tasks in a zero-shot manner? In this work, we present a functional reward encoding (FRE) as a general, scalable solution to this zero-shot RL problem. Our main idea is to learn functional representations of any arbitrary tasks by encoding their state-reward samples using a transformer-based variational auto-encoder. This functional encoding not only enables the pre-training of an agent from a wide diversity of general unsupervised reward functions, but also provides a way to solve any new downstream tasks in a zero-shot manner, given a small number of reward-annotated samples. We empirically show that FRE agents trained on diverse random unsupervised reward functions can generalize to solve novel tasks in a range of simulated robotic benchmarks, often outperforming previous zero-shot RL and offline RL methods. Code for this project is provided at: https://github.com/kvfrans/fre

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. fđť‘“fitalic_f-policy gradients: A general framework for goal conditioned rl using fđť‘“fitalic_f-divergences. arXiv preprint arXiv:2310.06794, 2023.
  2. Opal: Offline primitive discovery for accelerating offline reinforcement learning. arXiv preprint arXiv:2010.13611, 2020.
  3. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410, 2016.
  4. Modular multitask reinforcement learning with policy sketches. In International conference on machine learning, pp.  166–175. PMLR, 2017.
  5. Hindsight experience replay. Advances in neural information processing systems, 30, 2017.
  6. Successor features for transfer in reinforcement learning. Advances in neural information processing systems, 30, 2017.
  7. Universal successor features approximators. arXiv preprint arXiv:1812.07626, 2018.
  8. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  9. Caruana, R. Multitask learning. Machine learning, 28:41–75, 1997.
  10. Actionable models: Unsupervised offline reinforcement learning of robotic skills. arXiv preprint arXiv:2104.07749, 2021.
  11. Self-supervised reinforcement learning that transfers using random features. arXiv preprint arXiv:2305.17250, 2023.
  12. Dayan, P. Improving generalization for temporal difference learning: The successor representation. Neural computation, 5(4):613–624, 1993.
  13. Offline meta reinforcement learning–identifiability challenges and effective data collection strategies. Advances in Neural Information Processing Systems, 34:4607–4618, 2021.
  14. Rl22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT: Fast reinforcement learning via slow reinforcement learning. arXiv preprint arXiv:1611.02779, 2016.
  15. Adversarial intrinsic motivation for reinforcement learning. Advances in Neural Information Processing Systems, 34:8622–8636, 2021.
  16. Diversity is all you need: Learning skills without a reward function. arXiv preprint arXiv:1802.06070, 2018.
  17. Contrastive learning as goal-conditioned reinforcement learning. Advances in Neural Information Processing Systems, 35:35603–35620, 2022.
  18. Dher: Hindsight experience replay for dynamic goals. In International Conference on Learning Representations, 2018.
  19. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020.
  20. Conditional neural processes. In International conference on machine learning, pp.  1704–1713. PMLR, 2018a.
  21. Neural processes. arXiv preprint arXiv:1807.01622, 2018b.
  22. Contextual markov decision processes. arXiv preprint arXiv:1502.02259, 2015.
  23. Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359–366, 1989.
  24. Unsupervised behavior extraction via random intent priors. arXiv preprint arXiv:2310.18687, 2023.
  25. Kaelbling, L. P. Learning to achieve goals. In IJCAI, volume 2, pp.  1094–8. Citeseer, 1993.
  26. Attentive neural processes. arXiv preprint arXiv:1901.05761, 2019.
  27. Segment anything. arXiv preprint arXiv:2304.02643, 2023.
  28. Offline reinforcement learning with implicit q-learning. arXiv preprint arXiv:2110.06169, 2021.
  29. Conservative q-learning for offline reinforcement learning. Advances in Neural Information Processing Systems, 33:1179–1191, 2020.
  30. Cic: Contrastive intrinsic control for unsupervised skill discovery. arXiv preprint arXiv:2202.00161, 2022.
  31. Learning multi-level hierarchies with hindsight. arXiv preprint arXiv:1712.00948, 2017.
  32. Generalized hindsight for reinforcement learning. Advances in neural information processing systems, 33:7754–7767, 2020a.
  33. Multi-task batch reinforcement learning with metric learning. Advances in Neural Information Processing Systems, 33:6197–6210, 2020b.
  34. Hierarchical planning through goal-conditioned offline reinforcement learning. IEEE Robotics and Automation Letters, 7(4):10216–10223, 2022.
  35. Focal: Efficient fully-offline meta-reinforcement learning via distance metric learning and behavior regularization. arXiv preprint arXiv:2010.01112, 2020c.
  36. Visual reinforcement learning with imagined goals. Advances in neural information processing systems, 31, 2018.
  37. Hiql: Offline goal-conditioned rl with latent states as actions. arXiv preprint arXiv:2307.11949, 2023a.
  38. Metra: Scalable unsupervised rl with metric-aware abstraction. arXiv preprint arXiv:2310.08887, 2023b.
  39. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning, pp.  2778–2787. PMLR, 2017.
  40. Accelerating reinforcement learning with learned skill priors. In Conference on robot learning, pp.  188–204. PMLR, 2021.
  41. Offline meta-reinforcement learning with online self-supervision. In International Conference on Machine Learning, pp.  17811–17829. PMLR, 2022.
  42. Efficient off-policy meta-reinforcement learning via probabilistic context variables. In International conference on machine learning, pp.  5331–5340. PMLR, 2019.
  43. Semi-parametric topological memory for navigation. arXiv preprint arXiv:1803.00653, 2018.
  44. Universal value function approximators. In International conference on machine learning, pp.  1312–1320. PMLR, 2015.
  45. Dynamics-aware unsupervised discovery of skills. arXiv preprint arXiv:1907.01657, 2019.
  46. Perceiver-actor: A multi-task transformer for robotic manipulation. In Conference on Robot Learning, pp.  785–799. PMLR, 2023.
  47. Lancon-learn: Learning with language to enable generalization in multi-task manipulation. IEEE Robotics and Automation Letters, 7(2):1635–1642, 2021.
  48. Multi-task reinforcement learning with context-based representations. In International Conference on Machine Learning, pp.  9767–9779. PMLR, 2021.
  49. Learning more skills through optimistic exploration. arXiv preprint arXiv:2107.14226, 2021.
  50. Deepmind control suite. arXiv preprint arXiv:1801.00690, 2018.
  51. The information bottleneck method. arXiv preprint physics/0004057, 2000.
  52. Learning one representation to optimize all rewards. Advances in Neural Information Processing Systems, 34:13–23, 2021.
  53. Does zero-shot reinforcement learning exist? arXiv preprint arXiv:2209.14935, 2022.
  54. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  55. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pp.  1096–1103, 2008.
  56. Optimal goal-reaching reinforcement learning via quasimetric learning. arXiv preprint arXiv:2304.01203, 2023.
  57. No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82, 1997.
  58. Rethinking goal-conditioned supervised learning and its connection to offline rl. arXiv preprint arXiv:2202.04478, 2022.
  59. Don’t change the algorithm, change the data: Exploratory data for offline reinforcement learning. arXiv preprint arXiv:2201.13425, 2022.
  60. Robust task representations for offline meta-reinforcement learning via contrastive learning. In International Conference on Machine Learning, pp.  25747–25759. PMLR, 2022.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Kevin Frans (16 papers)
  2. Seohong Park (18 papers)
  3. Pieter Abbeel (372 papers)
  4. Sergey Levine (531 papers)
Citations (6)
View on Semantic Scholar

Summary

  • The paper introduces FRE as a novel method for unsupervised zero-shot reinforcement learning by encoding reward functions into a latent space.
  • It employs a two-step training process with variational latent encoding and offline policy development to generalize across diverse tasks.
  • Empirical evaluations on benchmarks like AntMaze and Kitchen show that FRE matches or outperforms state-of-the-art approaches.

Insights into Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings

The paper introduces a novel approach to zero-shot reinforcement learning (RL) using a technique called Functional Reward Encodings (FRE). The central question addressed is whether a generalist agent can be pre-trained on a set of unlabeled trajectories so that it can adapt to downstream tasks without further training. This is critical in enabling agents to efficiently transfer learned behaviors to new tasks in diverse domains such as robotics and autonomous systems.

Functional Reward Encodings

The authors propose FRE as a versatile solution to zero-shot RL, leveraging the encoding of arbitrary reward functions into a latent space. The FRE approach diverges from prior methods which relied on domain-specific representations or restricted reward structures. Traditional representations in zero-shot or multi-task RL often involve complex task-specific data annotation, whereas FRE opts for a more generalized and scalable approach using transformers—a move that aligns with advancements in unsupervised learning seen in other domains such as language and vision.

The methodology hinges on a two-step training process. Initially, a latent representation of possible reward functions is learned through a neural network architecture inspired by variational principles, which aim to maximize the amount of information the latent representation retains about the reward while minimizing its complexity. Following this, a policy is trained offline using the FRE-derived representations. The distinction and advantage of this approach lie in the utilization of latent encodings to address varied downstream tasks seamlessly.

Empirical Evaluation

The FRE framework is empirically validated using evaluations on standard offline RL benchmarks, such as AntMaze, the ExORL dataset, and the Kitchen environment from D4RL. These cover a spectrum of tasks involving locomotion and manipulation, which are pivotal for real-world applications. The results indicate that FRE outperforms or matches state-of-the-art methods on tasks including goal-reaching, directional movement, and structured locomotion paths. Notably, its performance is characterized by the ability to generalize across a wider set of tasks compared to other methods, such as successor features (SF) or the Forward-Backward method.

Implications and Future Directions

The introduction of FRE marks a significant stride in the pursuit of effective zero-shot RL, with implications that span both theoretical and practical realms. FRE’s advantage in learning from broad, unsupervised data could revolutionize how RL agents are developed, particularly those operating in environments with infrequent or delayed reward signals. By facilitating generalist agents capable of swift adaptation to new tasks, this approach aligns closely with the long-term goals of artificial general intelligence.

Looking forward, several avenues for research emerge. These include refining the design of the prior reward distribution to further enhance generalization capabilities, extending the approach to online settings, and exploring its application in domains with complex reward dynamics, such as real-world robotic systems or interactive environments. Furthermore, understanding the limits and capacity of functional reward encodings can drive innovations not just in RL but across adjacent fields like meta-learning and continual learning.

In conclusion, FRE presents a scalable and robust method for fostering adaptability in RL agents without arduous task-specific tuning. As the field advances, methodologies like FRE will be pivotal in bridging the gap between theoretical RL constructs and practical, deployable AI systems.