Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose
Go
al-conditioned
f
-
A
dvantage
R
egression (GoFAR), a novel regression-based offline GCRL algorithm derived from a state-occupancy matching perspective; the key intuition is that the goal-reaching task can be formulated as a state-occupancy matching problem between a dynamics-abiding imitator agent and an expert agent that directly teleports to the goal. In contrast to prior approaches, GoFAR does not require any hindsight relabeling and enjoys uninterleaved optimization for its value and policy networks. These distinct features confer GoFAR with much better offline performance and stability as well as statistical performance guarantee that is unattainable for prior methods. Furthermore, we demonstrate that GoFAR's training objectives can be re-purposed to learn an agent-independent goal-conditioned planner from purely offline source-domain data, which enables zero-shot transfer to new target domains. Through extensive experiments, we validate GoFAR's effectiveness in various problem settings and tasks, significantly outperforming prior state-of-art. Notably, on a real robotic dexterous manipulation task, while no other method makes meaningful progress, GoFAR acquires complex manipulation behavior that successfully accomplishes diverse goals.
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Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
We propose State Matching Offline Distribution Correction Estimation
(SMODICE), a novel and versatile regression-based offline imitation learning
(IL) algorithm derived via state-occupancy matching. We show that the SMODICE
objective admits a simple optimization procedure through an application of
Fenchel duality and an analytic solution in tabular MDPs. Without requiring
access to expert actions, SMODICE can be effectively applied to three offline
IL settings: (i) imitation from observations (IfO), (ii) IfO with dynamics or
morphologically mismatched expert, and (iii) example-based reinforcement
learning, which we show can be formulated as a state-occupancy matching
problem. We extensively evaluate SMODICE on both gridworld environments as well
as on high-dimensional offline benchmarks. Our results demonstrate that SMODICE
is effective for all three problem settings and significantly outperforms prior
state-of-art.
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- Award ID(s):
- 1918839
- NSF-PAR ID:
- 10404363
- Date Published:
- Journal Name:
- Proceedings of the 39 th International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\textbf{Go}$al-conditioned $f$-$\textbf{A}$dvantage $\textbf{R}$egression (GoFAR), a novel regression-based offline GCRL algorithm derived from a state-occupancy matching perspective; the key intuition is that the goal-reaching task can be formulated as a state-occupancy matching problem between a dynamics-abiding imitator agent and an expert agent that directly teleports to the goal. In contrast to prior approaches, GoFAR does not require any hindsight relabeling and enjoys uninterleaved optimization for its value and policy networks. These distinct features confer GoFAR with much better offline performance and stability as well as statistical performance guarantee that is unattainable for prior methods. Furthermore, we demonstrate that GoFAR's training objectives can be re-purposed to learn an agent-independent goal-conditioned planner from purely offline source-domain data, which enables zero-shot transfer to new target domains. Through extensive experiments, we validate GoFAR's effectiveness in various problem settings and tasks, significantly outperforming prior state-of-art. Notably, on a real robotic dexterous manipulation task, while no other method makes meaningful progress, GoFAR acquires complex manipulation behavior that successfully accomplishes diverse goals.more » « less
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