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| Attribute | Value |
|---|---|
| Model | Imagination-Augmented Agents (I2A) |
| Lab / Organization | DeepMind |
| Category | Model-Based RL |
| Subtype | Imagination-Augmented Policy |
| World Model Type | Imagination-augmented model-free agent |
| Primary Domain | Atari / Planning |
| Architecture | Model-free policy augmented with rollout encoder over imagined trajectories |
| Modality | Visual |
| Training Method | Joint training of environment model and imagination-augmented policy network |
| Status | foundational |
| Year | 2017 |
| Performance Index | 45/100 (medium confidence, v1.1) |
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Imagination-Augmented Agents (I2A) combine model-free reinforcement learning with learned world models. The agent uses an environment model to generate imagined rollouts of possible futures, then aggregates these imagined trajectories as additional context for its policy. This allows the agent to benefit from planning-like behavior without fully committing to model-based control. I2A demonstrated that even imperfect learned models could improve policy quality when used as an augmentation rather than the sole basis for decision-making.
Imagination-Augmented Agents (I2A) is an imagination-augmented model-free agent developed by Google DeepMind in 2017 for atari / planning.
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| Signal | Value |
|---|---|
| Definition | Imagination-Augmented Agents (I2A) is an imagination-augmented model-free agent developed by Google DeepMind in 2017 for atari / planning. |
| Short Description | An agent architecture that augments model-free policies with learned imagination rollouts from an environment model. |
| Benchmark Rows | 2 |
| FAQ Entries | 2 |
| Related Models | 4 |
| Related Guides | 0 |
| Related Research Topics | 1 |
| Last Updated | 2026-01-20 |
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| Reference | Link |
|---|---|
| Weber et al., 2017. Imagination-Augmented Agents for Deep Reinforcement Learning. NeurIPS 2017. | Open source |
Nearby models linked from the current editorial record.
| Model | Category | World Model Type | Index v1.1 |
|---|---|---|---|
| MuZero | Model-Based RL | Abstract learned dynamics + MCTS | 78/100 |
| DreamerV3 | Model-Based RL | Imagination-based dynamics model | 88/100 |
| Predictron | Model-Based RL | Abstract internal dynamics model | 43/100 |
| World Models (Ha & Schmidhuber) | Model-Based RL | Generative latent dynamics model | 48/100 |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| Model-Based RL vs Model-Free RL | Model-Based RL vs Model-Free RL | Model-based RL learns a world model for imagination-based planning. Model-free RL learns directly from interaction without an internal model. Each approach has distinct strengths depending on the application domain. |
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| Topic | Summary |
|---|---|
| Model-Based Reinforcement Learning | What model-based reinforcement learning is, how world models enable imagination-based planning, and why Dreamer, MuZero, PlaNet, and TD-MPC2 matter. |
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I2As generate imagined future trajectories using a learned environment model, encode them with a rollout encoder, and feed the encoded imaginations as additional inputs to the policy, augmenting rather than replacing model-free decision-making.
Learned environment models are imperfect. I2As use imagination as supplementary information rather than relying on it entirely, making the system robust to model errors.
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Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
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