EvIL: Evolution Strategies for Generalisable Imitation Learning

EvIL: Evolution Strategies for Generalisable Imitation Learning

Overview

The paper "EvIL: Evolution Strategies for Generalisable Imitation Learning" addresses a critical challenge in Imitation Learning (IL): transferring learned policies from one environment to a different, but related, environment. The authors highlight inherent limitations in modern deep IL algorithms which often result in ineffective policy optimization, especially in scenarios with varying environment dynamics.

Two primary concerns are identified: the recovered reward functions from these methods frequently induce suboptimal behaviors in new environments and the shaping of these rewards often necessitates extensive interaction. To address these, the authors propose novel and scalable fixes that are integrated into an algorithm called EvIL (EVolution strategies for Imitation Learning).

Problem Statement

The problem arises when expert demonstrations are collected in one environment (e.g., simulations) but need to be deployed in another (e.g., the real world). Modern deep IL algorithms often fail to generalize effectively due to:

1. Rewards that don't induce expert-level policies even in the environment they were trained in.
2. Poorly shaped rewards that require extensive environment interactions for optimization.

Proposed Solutions

The authors introduce several strategies to overcome these issues, creating a new methodology that combines the strengths of modern and classical IL approaches:

1. Reward Model Ensembles: To ensure the learned reward functions are generalizable across various policies, the authors propose using ensembles of reward models. This ensemble approach allows for a more robust estimation.
2. Random Policy Resets: By resetting policies occasionally, the algorithm avoids premature convergence to suboptimal strategies and ensures more diverse state space exploration.
3. Evolutionary Strategies for Reward Shaping: The authors apply Evolution Strategies (ES) to optimize a potential-based shaping term that significantly enhances the efficiency of policy retraining. This method directly optimizes for efficient retraining, addressing the gap left by classical IRL.

Methodology

The proposed algorithm, EvIL, involves a two-stage process:

IRL++:

• Adjustments such as maintaining a policy buffer, utilizing policy and discriminator ensembles, and introducing policy resets. These adjustments ensure the reward functions learned during IRL permit effective retraining.
• Evolution-Based Shaping:
• Post-IRL++, an ES-based method evolves a shaping term that optimizes the area under the curve (AUC) of reward performance during training. This approach ensures the reward shaping term significantly increases retraining efficiency.

These steps culminate in an algorithm that leverages the interaction efficiency of modern primal methods and the retraining efficacy of classical dual methods.

Numerical Results

The experimental results showcase the efficacy of EvIL across various MuJoCo environments:

Shaping RL:

• The ES-based shaping term significantly improved interaction efficiency over standard RL methods, often outperforming even the strong baseline of using the expert value function as a shaping term.
• IRL++ Retraining:
• On naïve retraining with the final IRL reward, policies often stagnate at suboptimal performance levels. However, IRL++ adjustments permitted effective retraining to near-expert performance levels.
• EvIL Efficiency:
• EvIL consistently led to faster and more effective retraining across various environments. Specific tasks involving dynamic randomization and stochastic actions demonstrate the method's robustness and generalizability.

For the transfer tasks, where the environments featured altered dynamics or stochastic interruptions, EvIL markedly outperformed both Behavioral Cloning (BC) and unshaped IRL. This confirms its robustness in handling unseen state distributions and variants of the original environment.

Practical Implications

The practical implications of EvIL are substantial for real-world applications of IL:

• Robust Transfer Learning: EvIL's ability to effectively transfer policies under dynamic and stochastic variations is crucial for autonomous systems operating in unpredictable real-world settings.
• Reduced Dependency on Simulations: By learning more generalized and efficiently shaped reward functions, EvIL reduces the extensive need for high-fidelity simulations during training phases.
• Enhanced Efficiency: The use of evolution strategies for reward shaping can lead to significant savings in both time and computational resources by optimizing the training process.

Future Directions

The research opens avenues for further exploration:

• Sim2Real Transfer: Extending and validating EvIL's approach in more complex real-world scenarios to bridge the simulation-to-reality gap.
• Integration with Model-Based Methods: Combining ES-based shaping within model-based reinforcement learning frameworks to potentially reduce the interaction cost even further.
• Scaling and Diversity: Testing EvIL on a wider variety of tasks and environments, including multi-agent systems, to further validate generalizability and robustness.

Conclusion

The paper provides a comprehensive analysis and solution to crucial challenges in generalizable imitation learning. By integrating ensemble methods, policy resets, and evolutionary strategies for reward shaping, EvIL effectively bridges the gap between theoretical efficacy and practical efficiency, demonstrating robust performance across a suite of continuous control tasks and variable environments. The results suggest significant advancements in the field of generalizable IL, with promising potential for real-world applications.