MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

Abstract: Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected exploration, i.e., select random sequences of actions. Exploration can also be directed using intrinsic rewards, such as curiosity or model epistemic uncertainty. However, effectively balancing task and intrinsic rewards is challenging and often task-dependent. In this work, we introduce a framework, MaxInfoRL, for balancing intrinsic and extrinsic exploration. MaxInfoRL steers exploration towards informative transitions, by maximizing intrinsic rewards such as the information gain about the underlying task. When combined with Boltzmann exploration, this approach naturally trades off maximization of the value function with that of the entropy over states, rewards, and actions. We show that our approach achieves sublinear regret in the simplified setting of multi-armed bandits. We then apply this general formulation to a variety of off-policy model-free RL methods for continuous state-action spaces, yielding novel algorithms that achieve superior performance across hard exploration problems and complex scenarios such as visual control tasks.

• The paper introduces MaxInfoRL, a framework that maximizes information gain to direct exploration over conventional random methods.
• It integrates intrinsic rewards with Boltzmann exploration to balance exploitation and improve learning efficiency in complex tasks.
• Experimental results demonstrate that MaxInfoRL achieves superior performance in high-dimensional and sparse-reward environments.

An Examination of Intrinsic Exploration in Reinforcement Learning via the BLACK Framework

The exploration-exploitation trade-off is a quintessential challenge in reinforcement learning (RL), where the balance between leveraging known information to maximize rewards and exploring new strategies to improve long-term outcomes is critically important. The paper "BLACK: Boosting Exploration in Reinforcement Learning through Information Gain Maximization" tackles this challenge by proposing a novel intrinsic-exploration-based framework designed to optimize this trade-off more effectively and efficiently than existing methods.

Core Contributions

The paper introduces the BLACK framework, which aims to enhance exploration in RL by maximizing the information gain regarding the underlying task. This approach differs from many traditional RL algorithms that rely predominantly on undirected, random exploration strategies such as $\epsilon$-greedy actions or Boltzmann exploration. In contrast, BLACK emphasizes informed exploration by steering the learning process towards informative transitions using intrinsic rewards that capture epistemic uncertainty and curiosity-driven signals.

Furthermore, the inclusion of Boltzmann exploration within the BLACK framework allows for a balanced trade-off between maximizing the value function and the entropy over states, actions, and rewards. By seamlessly integrating intrinsic with extrinsic exploration objectives, BLACK addresses efficiency issues in common exploration strategies and demonstrates its performance in both theoretical and practical scenarios.

Theoretical Insights and Practical Implications

The paper provides theoretical guarantees for BLACK's exploration strategy, particularly in the context of stochastic multi-armed bandits, demonstrating sublinear regret which is indicative of its potential efficacy in broader RL settings. This suggests that the framework efficiently balances exploration and exploitation over time, making it suitable for more complex problem domains.

Practically, BLACK's formulation has been applied successfully to various off-policy, model-free RL algorithms in continuous state-action spaces. The framework's tangible impact is evident across diverse environments, including standard RL benchmarks as well as challenging visual control tasks. BLACK consistently achieves superior performance, which highlights its robustness and adaptability to complex exploratory tasks such as those observed in visual control challenges like humanoid locomotion.

Experimental Evaluation and Results

Experimentally, BLACK has been rigorously evaluated against several baselines, showcasing its advancements over traditional methods. The framework excels in tasks characterized by sparse rewards and local optima, where naive exploration strategies falter. Moreover, it has demonstrated scalability to high-dimensional settings, such as state-of-the-art performance in visual control tasks, where information gain is particularly beneficial.

Potential for Future Research and Developments

BLACK represents a significant step forward in the exploration-exploitation landscape in RL but also opens several avenues for future research. The computational overhead associated with learning and maintaining an ensemble of forward-dynamics models for uncertainty estimation remains a challenge that might be mitigated through more computationally efficient approximations or models. Additionally, while BLACK is primarily implemented in a model-free, off-policy context, its principles could be extended to model-based RL scenarios, potentially further improving sample efficiency and learning convergence rates.

Advancements in intrinsic exploration methods like those proposed in BLACK may drive further innovations in adaptive decision-making systems across various domains, from robotics to autonomous systems and beyond. The framework's emphasis on trading off intrinsic and extrinsic signals in shaping exploration strategies may pave the way for more sophisticated and nuanced learning paradigms in artificial intelligence.