- The paper introduces a dual-timescale Planning Transformer that integrates planning tokens with auto-regressive action prediction to overcome long-horizon challenges.
- The methodology reduces compounding errors and improves credit assignment by combining high-level planning with low-level action sampling in complex environments.
- Empirical results demonstrate state-of-the-art performance and enhanced interpretability, setting a new standard for offline RL tasks.
Overview of "Planning Transformer: Long-Horizon Offline Reinforcement Learning with Planning Tokens" by Joseph Clinton and Robert Lieck
The paper "Planning Transformer: Long-Horizon Offline Reinforcement Learning with Planning Tokens" introduces a novel approach to offline reinforcement learning (Offline RL) that combines the advantages of Reinforcement learning via supervised learning (RvS) and Hierarchical Reinforcement Learning (HRL) to address the critical challenges of long-horizon tasks: compounding error and credit assignment.
Introduction to the Concept
Conventional RvS methodologies like Decision Transformers (DT) have shown success in handling continuous environments and sparse rewards. These approaches model the RL problem as a sequence prediction task, thereby excelling in sample efficiency and resilience to distractor signals. However, such models struggle with long-horizon tasks due to the compounding error inherent in auto-regressive predictions and the difficulty in effective credit assignment without long contexts. HRL methods offer a solution through hierarchical decomposition and sub-tasking but introduce additional complexity and difficulties in training and credit assignment between high and low-level policies.
Primary Contributions
The primary contribution of this paper is the introduction of the Planning Transformer (PT) framework, which extends the DT approach by incorporating "Planning Tokens". These tokens act as high-level, temporally extended sub-goals that guide the agent's policy. This dual time-scale approach reduces the effective action horizon, significantly mitigating the compounding error and enhancing long-term credit assignment.
The paper highlights three key contributions:
- Dual-Timescale Token Prediction: The model predicts both high-level Planning Tokens and low-level actions to effectively manage long-horizon tasks.
- State-of-the-Art Offline RL Performance: Empirical results demonstrate that the PT model outperforms existing state-of-the-art methods in both long and short-horizon tasks across various complex environments.
- Advancements in Interpretability: The explicit planning component improves the interpretability of the model’s policy through visualization techniques and attention maps.
Methodology
Plan Representation and Sampling
The model leverages a novel approach to generate Planning Tokens. Instead of the conventional autoencoder methods used in HRL, the PT model selects sparse future states from the trajectory to form its Plans. This simple selection method proves surprisingly effective, possibly because the unified training allows the model to optimize Plans that benefit the action prediction policy.
Input Sequence Construction
These generated Plans are integrated with the trajectory in a way that conditions the next action prediction on its forecast of future states, significantly reducing the effective action horizon and managing compounding errors. The plans are constructed by appending them to the trajectory sequence, ensuring that they contain information relevant to the agent’s current state and intended future actions.
Training and Inference
The PT model uses a unified training pipeline wherein both the action prediction and plan generation are optimized simultaneously. The combined loss function includes terms for both the action prediction and the plan deviation, balancing the learning process. During inference, the model generates a Plan before proceeding with the auto-regressive action generation, periodically recalibrating the Plan as the agent progresses through its environment.
Experimental Results
The PT model was evaluated across several environments, demonstrating its robustness and effectiveness. The environments include:
- Gym-MuJoCo: Reward-conditioned tasks such as HalfCheetah, Hopper, and Walker2d for varying grades of trajectory quality.
- AntMaze: Long-horizon, goal-conditioned tasks where an agent navigates mazes of varying complexity.
- FrankaKitchen: Highly complex, goal-conditioned tasks involving a multi-layered robot performing kitchen tasks.
The PT model achieved competitive or better results compared to state-of-the-art RL methods in all these environments. It was particularly effective in long-horizon, goal-conditioned tasks, substantially outperforming other models in AntMaze and FrankaKitchen settings.
Interpretability
One of the notable benefits of the PT model is its improved interpretability. The generation of Planning Tokens allows for a visual representation of the agent’s high-level considerations and strategies, facilitating a clearer understanding of the model’s decisions. This is a significant advancement over other hierarchical models where high-level decisions often lack transparency.
Implications and Future Work
The introduction of Planning Tokens within a transformer framework opens several new avenues for future research:
- Extension to Online Learning: Adapting the PT framework for online RL environments could leverage its dynamic plan generation capabilities for real-time learning and adaptation.
- Non-Markovian Environments: Enhancements in managing environments with long-term dependencies, possibly incorporating temporal encoders, could expand the application of PT.
- Application to LLMs: Given the structural similarities, exploration of PT within LLMs could potentially improve their long-term and complex reasoning capabilities.
The Planning Transformer represents a significant stride towards integrating hierarchical planning with sequence modeling in RL, offering both practical performance improvements and enhanced interpretability. This work positions PT as a promising framework for tackling the intricacies of long-horizon tasks in RL.
Conclusion
The paper successfully demonstrates that the Planning Transformer, with its dual-timescale architecture and Planning Tokens, effectively addresses the challenges of compounding error and long-term credit assignment in complex RL environments. Through thorough experimental validation and clear advancements in interpretability, the PT model sets a new standard for long-horizon Offline RL tasks while providing a robust framework for future research.