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Towards Principled Representation Learning from Videos for Reinforcement Learning
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Theoretical analysis of video-based representation pre-training (autoencoding, forward modeling, temporal contrastive) for RL in Block MDPs, showing polynomial sample complexity without exogenous noise and an exponential lower bound with exogenous noise, validated on GridWorld and ViZDoom.
Problem
Whether representations learned from action-free video data provably recover latent states and enable efficient downstream RL, especially in the presence of temporally correlated exogenous noise.
Approach
Provides finite-sample upper bounds for forward modeling and temporal contrastive learning on Block MDPs with only iid noise, and a lower bound showing that with exogenous noise, compact frozen video-pretrained decoders cannot support efficient RL, unlike action-labeled trajectory pretraining.
Key results
- Forward modeling and temporal contrastive representations enable successful PPO RL with no exogenous noise, supporting the upper-bound theory.
- With exogenous noise, temporal contrastive learning completely fails in experiments while forward modeling degrades more gradually.
- ACRO, trained on action-labeled trajectories, achieves the strongest performance across tasks, consistent with the exponential separation lower bound.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use forward modeling or temporal contrastive video pre-training without exogenous noise | Video-pretrained representations with PPO · GridWorld and ViZDoom | RL success | Helped |
| Use autoencoding for video representation pre-training | Video-pretrained representations with PPO · ViZDoom | RL performance | Helped |
| Use autoencoding for video representation pre-training | Video-pretrained representations with PPO · GridWorld | RL performance | Hurt |
| Add exogenous observation noise | Forward modeling video representations · GridWorld and ViZDoom | RL performance | Mixed |
| Add exogenous observation noise | Temporal contrastive video representations · GridWorld and ViZDoom | RL performance | Hurt |
| Increase the size or number of exogenous noise variables | Forward modeling video representations · GridWorld | RL performance | Hurt |
| Increase the number of iid noisy diamonds | Forward modeling and ACRO representations · GridWorld | RL performance | Hurt |
| Use ACRO trained with trajectory data instead of video-pretrained representations | Representation learning for downstream RL · GridWorld and ViZDoom tasks | RL performance | Helped |
Surprising findings
- Temporal contrastive learning can minimize its loss by attending to exogenous noise, causing it to fail completely under exogenous noise.
- Autoencoder performance was unpredictable: good on ViZDoom but poor on GridWorld.
Limitations
- Upper bounds assume iid noise only; no finite-sample guarantee for autoencoders.
- Lower bound is for frozen compact decoders; theory relies on realizability and margin assumptions.
Experimental setup
Datasets: GridWorld (Minigrid) video ViZDoom basic video ViZDoom Defend The Center video
Benchmarks: Minigrid GridWorld 12x12 ViZDoom basic ViZDoom Defend The Center
Metrics: episode return RL success under exogenous noise
Baselines: autoencoder forward modeling temporal contrastive learning ACRO
Builds on: Block MDP Ex-Block MDP ACRO
Closest papers by meaning
- Reinforcement Learning with Action-Free Pre-Training from Videos (ICML 2022 · similarity 0.90)
- Pretraining Representations for Data-Efficient Reinforcement Learning (NeurIPS 2021 · similarity 0.89)
- The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning (NeurIPS 2024 · similarity 0.88)
- Rich-Observation Reinforcement Learning with Continuous Latent Dynamics (ICML 2024 · similarity 0.88)
- Bridging State and History Representations: Understanding Self-Predictive RL (ICLR 2024 · similarity 0.88)
- Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning (NeurIPS 2026 · similarity 0.88)
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