Taming the Noise in Reinforcement Learning via Soft Updates (1512.08562v4)

Abstract: Model-free reinforcement learning algorithms, such as Q-learning, perform poorly in the early stages of learning in noisy environments, because much effort is spent unlearning biased estimates of the state-action value function. The bias results from selecting, among several noisy estimates, the apparent optimum, which may actually be suboptimal. We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process. We show that this method reduces the bias of the value-function estimation, leading to faster convergence to the optimal value and the optimal policy. Moreover, G-learning enables the natural incorporation of prior domain knowledge, when available. The stochastic nature of G-learning also makes it avoid some exploration costs, a property usually attributed only to on-policy algorithms. We illustrate these ideas in several examples, where G-learning results in significant improvements of the convergence rate and the cost of the learning process.

• The paper introduces G-learning, which applies an information cost penalty to soften early policy commits and mitigate bias in Q-value estimates.
• The method offers convergence guarantees by gradually reducing the penalty, allowing a shift from stochastic to deterministic policies as learning progresses.
• Empirical results demonstrate improved convergence rates and exploration efficiency in high-noise environments compared to traditional Q-learning.

An Overview of G-learning: Addressing Noise in Reinforcement Learning

The paper entitled "Taming the Noise in Reinforcement Learning via Soft Updates" introduces G-learning, a novel off-policy reinforcement learning algorithm aimed at mitigating the bias present in traditional model-free algorithms like Q-learning. This bias arises when early-stage learning in noisy environments wrongly commits to suboptimal policies, typically due to selecting seemingly optimal actions from noisy estimates. G-learning proposes an approach to regularizing these value estimates by incorporating a penalty that discourages deterministic policies during the initial phases of learning.

Core Contributions

The authors of this paper present a new methodology for reducing the estimation bias typically observed in Q-learning. Key contributions include:

• Regularization via Information Cost: By penalizing deterministic policies with an information cost term, G-learning incorporates stochastic policies during the initial stages of learning. As learning progresses, the penalty diminishes, allowing for more deterministic policies as sufficient evidence accumulates.
• Convergence Guarantee: The authors formulate G-learning with convergence proofs, ensuring that with appropriate scheduling of the penalty coefficient, the algorithm converges to the optimal value function akin to Q-learning. Furthermore, the method allows the integration of prior domain knowledge, enhancing its applicability in various environments.
• Empirical Validation: Through experimentation, G-learning demonstrates significant improvements in convergence rate compared to existing algorithms, especially in scenarios with high noise. Notably, it exhibits on-policy-like exploration efficiency while retaining the off-policy nature, an attribute often not seen in such algorithms.

Implications and Future Directions

The development of G-learning holds substantial theoretical and practical implications for reinforcement learning. Theoretically, it introduces a robust framework for addressing noise through the lens of information theory, which may be extended or adapted to other learning algorithms. Practically, the incorporation of stochastic exploration strategies influences applications where exploration costs are prohibitively high, offering more efficient learning strategies.

Potentially, this approach could predicate future advancements in reinforcement learning by:

1. Extending to Function Approximation: Applying G-learning principles where function approximation is critical, such as deep reinforcement learning, may lead to better performance in large-scale problems.
2. Combining with Other Techniques: Integrating G-learning into multi-agent systems or combining it with experience replay or hierarchical reinforcement learning could open new avenues for complex decision-making tasks.
3. Further Exploration of Scheduling Schemes: Investigating optimal scheduling strategies for the information penalty might unearth more dynamic approaches to bridging the gap from stochastic to deterministic policies efficiently.
4. Exploration in Dynamic and Multi-modal Environments: G-learning can be adapted for environments with non-stationary dynamics or those requiring multi-modal policies, potentially influencing robotics and autonomous systems.

In summary, this work brings a notable advancement to the field of reinforcement learning by correcting the inherent biases in value estimation processes under noisy conditions. By softening the policy determination initially and adapting as learning progresses, it offers a balanced approach that encourages efficient exploration while ensuring optimal policy convergence. Future investigations could further refine its theoretical underpinnings and broaden its scope of applications.