Robust Adversarial Reinforcement Learning (1703.02702v1)

Abstract: Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning is done in real world, the data scarcity leads to failed generalization from training to test scenarios (e.g., due to different friction or object masses). Inspired from H-infinity control methods, we note that both modeling errors and differences in training and test scenarios can be viewed as extra forces/disturbances in the system. This paper proposes the idea of robust adversarial reinforcement learning (RARL), where we train an agent to operate in the presence of a destabilizing adversary that applies disturbance forces to the system. The jointly trained adversary is reinforced -- that is, it learns an optimal destabilization policy. We formulate the policy learning as a zero-sum, minimax objective function. Extensive experiments in multiple environments (InvertedPendulum, HalfCheetah, Swimmer, Hopper and Walker2d) conclusively demonstrate that our method (a) improves training stability; (b) is robust to differences in training/test conditions; and c) outperform the baseline even in the absence of the adversary.

Robust Adversarial Reinforcement Learning

The paper "Robust Adversarial Reinforcement Learning" by Lerrel Pinto et al., addresses the pivotal challenge of generalization in Reinforcement Learning (RL). It focuses on two critical issues: the difficulty of transferring learned policies from simulation to real-world scenarios, and the scarcity of real-world data leading to poor generalization from training to test settings. The authors propose Robust Adversarial Reinforcement Learning (RARL) to mitigate these issues by incorporating adversarial disturbances during the training phase.

Problem Addressed

The central premise is that RL-based approaches often fail to generalize due to the significant gap between simulation and real-world environments and differences in training and test scenarios. Policy-learning in real-world settings is impeded by data scarcity, while simulations fail to mimic real-world physics accurately. To address these discrepancies, the paper introduces Robust Adversarial Reinforcement Learning.

Methodology

RARL leverages the concept of adversarial agents in which an adversary applies destabilizing forces to the system during training. The protagonist, or the main agent, is trained to operate robustly despite these disturbances. This setup is formulated as a zero-sum, minimax objective function where the adversary learns an optimal destabilization policy. The training, therefore, becomes a dynamic two-player game where both agents improve iteratively.

RARL's methodology can be outlined as follows:

1. Adversarial Agents for Modeling Disturbances: The adversary applies disturbances to the system, effectively sampling trajectories that represent worst-case scenarios.
2. Alternating Optimization: The learning process alternates between optimizing the protagonist's policy and the adversary's policy, ensuring that the protagonist's learned policy is robust to adversarial disturbances.

Experimental Evaluation

RARL was evaluated across multiple OpenAI Gym environments, including InvertedPendulum, HalfCheetah, Swimmer, Hopper, and Walker2D. The experiments showcased RARL's superior performance compared to standard TRPO algorithms in terms of training stability and robustness to variations in test conditions.

Key findings of the evaluation include:

• Robustness to Model Initialization: RARL demonstrated better performance under different model parameter initializations and random seeds, addressing data scarcity issues by reducing the sensitivity of learning.
• Performance in the Presence of Adversaries: The trained policies were robust to test environment disturbances, outperforming standard policies.
• Generalization to Variations: The methods showed significant resilience against changes in environmental conditions such as varying mass and friction coefficients during test phases.

Numerical Results

The numerical results were compelling:

• In the HalfCheetah environment, RARL achieved an average reward of $5444 \pm 97$, outperforming the baseline of $5093 \pm 44$.
• In the Walker2D environment, it achieved $5854 \pm 159$, whereas the baseline was $5418 \pm 87$.

Implications and Future Work

RARL presents a robust framework for training RL agents that are capable of handling real-world variability and uncertainties. This approach has significant implications for deploying RL in real-world scenarios where environmental factors are not entirely predictable. The adversarial training paradigm builds more resilient models that can adapt to unpredictable conditions and adversarial disturbances.

Future research directions could include:

• Extensions to Multi-Agent Systems: Investigating RARL in multi-agent settings where multiple protagonists and adversaries interact.
• Real-World Applications: Applying RARL to more complex real-world tasks beyond the simulated environments.
• Adaptive Adversarial Strategies: Developing dynamic adversarial strategies that evolve based on the protagonist's learning progress.

In conclusion, Robust Adversarial Reinforcement Learning offers a significant advancement in developing RL policies that generalize effectively across diverse and unpredictable real-world conditions, representing a pivotal step toward more reliable and practical AI applications.