Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions
In reinforcement learning, off-policy actor-critic approaches like DDPG and TD3 are based on the deterministic policy gradient. Herein, the Q-function is trained from off-policy environment data and the actor (policy) is trained to maximize the Q-function via gradient ascent. We observe that in complex tasks like dexterous manipulation and restricted locomotion, the Q-value is a complex function of action, having several local optima or discontinuities. This poses a challenge for gradient ascent to traverse and makes the actor prone to get stuck at local optima. To address this, we introduce a new actor architecture that combines two simple insights: (i) use multiple actors and evaluate the Q-value maximizing action, and (ii) learn surrogates to the Q-function that are simpler to optimize with gradient-based methods. We evaluate tasks such as restricted locomotion, dexterous manipulation, and large discrete-action space recommender systems and show that our actor finds optimal actions more frequently and outperforms alternate actor architectures.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deterministic Value-Policy Gradients
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexi…
continuous-controlContinuous ControlDeep Reinforcement Learningreinforcement-learning+2Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach
Compared to on-policy counterparts, off-policy model-free deep reinforcement learning can improve data efficiency by repeatedly using the previously gathered data. However, off-policy learning becomes challenging when th…
continuous-controlContinuous ControlDeep Reinforcement LearningQ-Learning+2Equivalence of stochastic and deterministic policy gradients
Policy gradients in continuous control have been derived for both stochastic and deterministic policies. Here we study the relationship between the two. In a widely-used family of MDPs involving Gaussian control noise an…
continuous-controlContinuous ControlPolicy Gradient MethodsDeterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
Current approaches to model-based offline reinforcement learning often incorporate uncertainty-based reward penalization to address the distributional shift problem. These approaches, commonly known as pessimistic value …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Variational InferenceRevisiting stochastic off-policy action-value gradients
Off-policy stochastic actor-critic methods rely on approximating the stochastic policy gradient in order to derive an optimal policy. One may also derive the optimal policy by approximating the action-value gradient. The…