Are Expressive Models Truly Necessary for Offline RL?

• URL: http://arxiv.org/abs/2412.11253v1
• Date: Sun, 15 Dec 2024 17:33:56 GMT
• Title: Are Expressive Models Truly Necessary for Offline RL?
• Authors: Guan Wang, Haoyi Niu, Jianxiong Li, Li Jiang, Jianming Hu, Xianyuan Zhan,
• Abstract summary: Sequential modeling requires capturing accurate dynamics across long horizons in trajectory data to ensure reasonable policy performance. We show that lightweight models as simple as shallow 2-layers can enjoy accurate dynamics consistency and significantly reduced sequential modeling errors.
• Score: 18.425797519857113
• License:
• Abstract: Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm. Sequential modeling, however, requires capturing accurate dynamics across long horizons in trajectory data to ensure reasonable policy performance. To meet this requirement, leveraging large, expressive models has become a popular choice in recent literature, which, however, comes at the cost of significantly increased computation and inference latency. Contradictory yet promising, we reveal that lightweight models as simple as shallow 2-layer MLPs, can also enjoy accurate dynamics consistency and significantly reduced sequential modeling errors against large expressive models by adopting a simple recursive planning scheme: recursively planning coarse-grained future sub-goals based on current and target information, and then executes the action with a goal-conditioned policy learned from data rela-beled with these sub-goal ground truths. We term our method Recursive Skip-Step Planning (RSP). Simple yet effective, RSP enjoys great efficiency improvements thanks to its lightweight structure, and substantially outperforms existing methods, reaching new SOTA performances on the D4RL benchmark, especially in multi-stage long-horizon tasks.

Related papers

• AdaCred: Adaptive Causal Decision Transformers with Feature Crediting [11.54181863246064]
  We introduce AdaCred, a novel approach that represents trajectories as causal graphs built from short-term action-reward-state sequences. Our experiments demonstrate that AdaCred-based policies require shorter trajectory sequences and consistently outperform conventional methods in both offline reinforcement learning and imitation learning environments.
  arXiv  Detail & Related papers  (2024-12-19T22:22:37Z)
• Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds [59.875550175217874]
  We show that a simple Model-based Reinforcement Learning scheme achieves strong regret and sample bounds in online and offline RL settings. We highlight that our algorithms are simple, fairly standard, and indeed have been extensively studied in the RL literature.
  arXiv  Detail & Related papers  (2024-08-16T19:52:53Z)
• Simplified Temporal Consistency Reinforcement Learning [19.814047499837084]
  We show that a simple representation learning approach relying on a latent dynamics model trained by latent temporal consistency is sufficient for high-performance RL. Our approach outperforms model-free methods by a large margin and matches model-based methods' sample efficiency while training 2.4 times faster.
  arXiv  Detail & Related papers  (2023-06-15T19:37:43Z)
• Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs [5.488334211013093]
  We show that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. We also show that iteratively updating the model is of major importance to avoid biases in the RL training.
  arXiv  Detail & Related papers  (2023-02-14T16:14:39Z)
• Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective [142.36200080384145]
  We propose a single objective which jointly optimize a latent-space model and policy to achieve high returns while remaining self-consistent. We demonstrate that the resulting algorithm matches or improves the sample-efficiency of the best prior model-based and model-free RL methods.
  arXiv  Detail & Related papers  (2022-09-18T03:51:58Z)
• Reinforcement Learning as One Big Sequence Modeling Problem [84.84564880157149]
  Reinforcement learning (RL) is typically concerned with estimating single-step policies or single-step models. We view RL as a sequence modeling problem, with the goal being to predict a sequence of actions that leads to a sequence of high rewards.
  arXiv  Detail & Related papers  (2021-06-03T17:58:51Z)
• Model-Based Offline Planning with Trajectory Pruning [15.841609263723575]
  offline reinforcement learning (RL) enables learning policies using pre-collected datasets without environment interaction. We propose a new light-weighted model-based offline planning framework, namely MOPP, which tackles the dilemma between the restrictions of offline learning and high-performance planning. Experimental results show that MOPP provides competitive performance compared with existing model-based offline planning and RL approaches.
  arXiv  Detail & Related papers  (2021-05-16T05:00:54Z)
• COMBO: Conservative Offline Model-Based Policy Optimization [120.55713363569845]
  Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable. We develop a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-actions. We find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods.
  arXiv  Detail & Related papers  (2021-02-16T18:50:32Z)
• Offline Reinforcement Learning from Images with Latent Space Models [60.69745540036375]
  offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. We build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces. Our approach is both tractable in practice and corresponds to maximizing a lower bound of the ELBO in the unknown POMDP.
  arXiv  Detail & Related papers  (2020-12-21T18:28:17Z)
• MOPO: Model-based Offline Policy Optimization [183.6449600580806]
  offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. We show that an existing model-based RL algorithm already produces significant gains in the offline setting. We propose to modify the existing model-based RL methods by applying them with rewards artificially penalized by the uncertainty of the dynamics.
  arXiv  Detail & Related papers  (2020-05-27T08:46:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.