… Deep reinforcement learning (deep RL) has emerged as a … entropy policies with approximate
inference for reinforcement … Our reinforcement learning problem can be defined as policy …

… The proposed APA is proven to be convergent even with a more aggressive learning rate, …
algorithm with deep Q-network (DQN), Double DQN and deep deterministic policy gradient (…

… in deep reinforcement learning research (eg the Arcade Learning Environment [3]). Our
findings suggest that policies … on a higher level, to the distribution of generated levels presented …

… Deep reinforcement learning (RL) has seen increased interest in recent years due to its …
the importance of hyperparameter tuning and the high level of stochasticity due to random seeds…

… policies, we use the notation µθ : S→A to denote the policy … , where higher level decisions
can reuse lower level skills (… where both lowlevel motor controls and high-level decisions are …

… Following the ideas discussed above, Figure 7 represents the general update scheme that
we propose to further improve the performance of deep reinforcement learning algorithms. …

… Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the
current policy… in the non-parameterized proximal policy space. Using supervised regression, it …

… Using SUMO and its associated traffic control interface (TraCI), we can access the vehicle
information in the road network, and execute highlevel decisions in the learner model and take …

… -deep deterministic policy gradient (h-DDPG). The proposed algorithm comprises two levels
of … learning methods to further improve the performance of top level hierarchy (which they …

… deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs.
We confirm that both policy … DGX-1 to learn successful strategies in Atari games in mere …