Reinforcement Learning with Unsupervised Auxiliary Tasks (1611.05397v1)

Abstract: Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible training signals. In this paper, we introduce an agent that also maximises many other pseudo-reward functions simultaneously by reinforcement learning. All of these tasks share a common representation that, like unsupervised learning, continues to develop in the absence of extrinsic rewards. We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task. Our agent significantly outperforms the previous state-of-the-art on Atari, averaging 880\% expert human performance, and a challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks leading to a mean speedup in learning of 10$\times$ and averaging 87\% expert human performance on Labyrinth.

• The paper proposes the UNREAL agent that augments standard RL with auxiliary tasks, significantly speeding up learning by reaching optimal performance in 10× fewer steps.
• It integrates the A3C framework with auxiliary control and reward prediction tasks to develop richer representations from sensorimotor data in complex environments.
• Empirical results demonstrate that the UNREAL agent achieves 87% of expert human performance in Labyrinth and 880% human-normalized performance on Atari, outperforming baseline methods.

Reinforcement Learning with Unsupervised Auxiliary Tasks

The paper "Reinforcement Learning with Unsupervised Auxiliary Tasks" by Jaderberg et al. explores advanced methodologies for enhancing deep reinforcement learning (RL) agents using unsupervised auxiliary tasks. This work introduces a novel RL agent, known as the UNREAL (UNsupervised REinforcement and Auxiliary Learning) agent, that significantly outperforms existing state-of-the-art methods, particularly in complex visual domains.

Core Contributions and Techniques

The primary contributions of this paper revolve around integrating auxiliary tasks to improve learning efficiency and robustness. The authors propose augmenting the classic reinforcement learning objective, which maximizes cumulative extrinsic rewards, with additional pseudo-reward functions. These pseudo-rewards are derived from the agent's sensorimotor data and assist in developing more effective representations, even in the absence of extrinsic rewards.

The architecture incorporates several key elements:

1. Asynchronous Advantage Actor-Critic (A3C) Framework: The foundational architecture is based on the A3C algorithm, which utilizes multiple parallel agents to interact with the environment, accelerating and stabilizing the learning process.
2. Auxiliary Control Tasks: These tasks involve learning to control features of the input, such as pixel modifications in specific regions of the visual input. The agent learns to maximize changes in these features, thus learning effective representations from the environment's dynamics.
3. Auxiliary Reward Prediction: In addition to control tasks, the agent predicts immediate rewards based on short-term historical context. This task facilitates faster identification of reward-relevant states by alleviating the sparsity of rewards in many environments.
4. Experience Replay: An experience replay mechanism is incorporated to enhance the efficiency and stability of learning from past experiences. The replay buffer is particularly useful for skewing the data distribution towards rewarding events, thereby enabling more robust value function approximation and policy learning.

Strong Numerical Results

The empirical evaluation of the UNREAL agent spans two challenging environments: the 3D Labyrinth domain and the Atari domain. The UNREAL agent demonstrates substantial improvements over the baseline A3C agent in both environments.

• Labyrinth: On average, the UNREAL agent achieves 87% of expert human performance, compared to 54% for the vanilla A3C agent. Notably, the learning speed is significantly enhanced, with the UNREAL agent reaching A3C's best performance in 10 times fewer steps on average.
• Atari: The UNREAL agent achieves an average of 880% human-normalized performance across 57 Atari games, a marked improvement over existing methods. Moreover, the agent exhibits greater robustness to hyperparameters, as evidenced by consistent performance across a range of hyperparameter settings.

Theoretical and Practical Implications

The paper's findings have profound implications for both theoretical research and practical applications of reinforcement learning:

1. Representation Learning: The integration of auxiliary tasks accentuates the significance of learning rich representations from the environment, even when extrinsic rewards are sparse or delayed. This can be leveraged for various applications beyond game playing, such as robotics and autonomous navigation, where sensory data is abundant, but rewards are infrequent.
2. Data Efficiency: By significantly reducing the number of training steps required to achieve optimal performance, this approach offers a pathway toward more data-efficient RL methods. This is crucial for real-world applications where collecting data can be expensive or time-consuming.
3. Robustness: The robustness to hyperparameter variations demonstrated by the UNREAL agent points to a more reliable and stable learning algorithm. This reliability is essential for deploying RL agents in dynamic and unpredictable real-world environments.

Future Developments

Future research could extend the principles outlined in this paper in various directions:

• Meta-Learning: Incorporating meta-learning techniques to automatically tune the auxiliary task weights and adapt them to different environments.
• Hierarchical Reinforcement Learning: Further exploration of hierarchical RL, where auxiliary control tasks could dynamically adjust based on higher-level task goals.
• Transfer Learning: Applying the learned representations and policies across different domains to enhance transfer learning capabilities in RL agents.

In conclusion, this paper by Jaderberg et al. makes significant strides in advancing the capabilities of deep reinforcement learning agents through the innovative use of unsupervised auxiliary tasks. The demonstrated improvements in both efficiency and robustness lay a strong foundation for future research and applications in various complex domains.