Integrate Reinforcement Learning with Counterfactual Reasoning Architecture

[csmangum]

Our current agent memory architecture effectively compresses experiences through embeddings, but we need to better leverage the "imagination" capabilities for reinforcement learning. This issue explores concrete approaches to integrate RL with counterfactual reasoning, moving beyond just using decompressed memories during replay to actively harnessing imagination for agent learning and decision-making.

Approaches to Explore

1. Imagination-Augmented Experience Replay

Concept: Extend standard experience replay with synthetically generated experiences from counterfactual reasoning.

Implementation Ideas:

• Generate variations of stored experiences by manipulating embedding vectors before decoding
• Sample from both actual and counterfactual experiences proportionally during training
• Develop a strategy to balance real vs. imagined experiences based on confidence in counterfactual accuracy

Research Questions:

• What's the optimal ratio of real to counterfactual experiences?
• How does fidelity of counterfactual reconstruction affect learning?
• Can we weight counterfactual experiences differently in the loss function?

2. Counterfactual Policy Evaluation

Concept: Evaluate actions the agent didn't actually take through embedding manipulation.

Implementation Ideas:

• For each stored state s_t and action a_t, generate embedding representations of "what if I had taken a different action a_j"
• Use these counterfactuals to improve off-policy learning
• Create an importance sampling mechanism that accounts for the "realism" of counterfactual outcomes

Research Questions:

• Can we learn a reliable transformation matrix for different actions?
• How accurately can we predict alternative action outcomes?
• Does this approach reduce the well-known overestimation bias in Q-learning?

3. Embedding-Based World Models

Concept: Use our embedding space as an implicit world model for planning and simulation.

Implementation Ideas:

• Train transition functions that operate directly in embedding space (s_t embedding → a_t → predicted s_t+1 embedding)
• Use these transitions for n-step planning without full state reconstruction
• Develop a hybrid approach that operates primarily in embedding space but occasionally decodes to validate predictions

Research Questions:

• Is planning in embedding space more efficient than in raw state space?
• How do errors propagate during multi-step predictions in embedding space?
• Can we develop embedding-specific planning algorithms?

4. Curiosity-Driven Exploration via Counterfactuals

Concept: Guide exploration to generate experiences in unexplored regions of the counterfactual space.

Implementation Ideas:

• Define novelty based on distances in embedding space
• Generate counterfactual states that would be "interesting" to experience
• Create an intrinsic reward for visiting states that fill gaps in the embedding space

Research Questions:

• What's the most effective distance metric in embedding space for novelty detection?
• How to balance exploration of novel real states vs. novel counterfactual states?
• Can we predict which regions of embedding space will yield valuable learning?

5. Hindsight Experience Manipulation

Concept: Generate multiple counterfactual goal scenarios from a single trajectory.

Implementation Ideas:

• Extend Hindsight Experience Replay by manipulating relevant dimensions in embedding space
• Create more varied "imagined" goals beyond those physically encountered
• Develop goal embeddings that can be systematically modified

Research Questions:

• How to identify the "goal dimensions" in our embedding space?
• What's the right strategy for generating useful counterfactual goals?
• How does this approach compare to standard HER in sample efficiency?

6. Risk-Aware Planning via Counterfactuals

Concept: Anticipate potential negative outcomes through counterfactual simulations.

Implementation Ideas:

• Learn "risk transformation vectors" that can be applied to current state embeddings
• Before executing actions, apply these transformations to identify potentially dangerous outcomes
• Develop a risk-sensitive policy optimization algorithm using these simulations

Research Questions:

• Can we reliably identify risky state patterns in embedding space?
• How to balance risk aversion with performance optimization?
• Can this approach reduce catastrophic failures during training?

7. Embedding-Based Value Approximation

Concept: Train value functions directly on the embedding space rather than raw states.

Implementation Ideas:

• Use the compressed embedding vector as input to value/policy networks
• Explore architectures that can leverage the semantic structure of the embedding space
• Investigate if this improves generalization across similar states

Research Questions:

• Does this approach improve value function approximation?
• Can we interpret the relationship between embedding dimensions and value?
• Does this facilitate transfer learning between related tasks?

Implementation Priority

Suggested order of implementation and experimentation:

1. Embedding-Based Value Approximation (easiest to implement, foundation for others)
2. Imagination-Augmented Experience Replay (direct extension of current replay mechanism)
3. Counterfactual Policy Evaluation (builds on the first two)
4. Embedding-Based World Models (more complex but potentially highest payoff)
5. Remaining approaches

Metrics and Evaluation

Key metrics to track:

• Sample efficiency (learning curves vs. environment steps)
• Asymptotic performance (final policy quality)
• Generalization to novel scenarios
• Computational overhead of counterfactual generation and usage
• Quality/plausibility of generated counterfactuals

Resources & References

Relevant papers:

• Imagination-Augmented Agents for Deep Reinforcement Learning (Weber et al., 2017)
• Hindsight Experience Replay (Andrychowicz et al., 2017)
• World Models (Ha & Schmidhuber, 2018)
• Counterfactual Multi-Agent Policy Gradients (Foerster et al., 2018)
• Curious Model-Learning for RL (Pathak et al., 2017)

Notes

This exploration represents a significant advancement of our architecture from primarily a memory system to a full cognitive architecture that can imagine, plan, and learn from hypothetical experiences.

The most promising direction likely involves a hybrid approach that combines several of these ideas, using the embedding space as a unified substrate for memory, imagination, and learning.