… , deep learning is enabling reinforcement learning (RL) to scale to problems that were previously
intractable, such as learning to … policy iteration, where policy iteration consists of policy …

… In this section, we will define the reinforcement learning problem that we are addressing and
briefly summarize the maximum entropy policy … Maximum Entropy Reinforcement Learning …

… Deep reinforcement learning has shown great success in automatic policy learning for …
policies, whereas our method is applicable to DRL policies with arbitrary network structures. …

… background of machine learning, deep learning and reinforcement learning in Section 2.
Next we discuss core RL elements, including value function in Section 3.1, policy in Section 3.2…

… However, as the problems we task reinforcement learning agents with become ever more
complex, it may be beneficial (and even necessary) to gradually acquire skills over multiple …

… on the policy π, and may be stochastic. The goal in reinforcement learning is to learn a policy
which maximizes the expected return from the start distribution J = Eri,si∼E,ai∼π [R1]. We …

… within the standard reinforcement-learning formalism. The … in reinforcement learning: policy
improvement and policy … , we can reduce a reinforcement-learning problem to a simpler …

… given policy π, over the sampled trajectories guided by the policy. We call this “on-policy”
manner as in reinforcement learning the policy … is conditioned on or estimated by current policy. …

… (SimPLe), that utilizes these video prediction techniques and trains a policy to play the … ,
where the policy is deployed to collect more data in the original game, we learn a policy that, for …

… state-values and policies learned by tabular Q-learning overlaid on our … policies that are
limited to a fraction of the state space. In Section 3, we show that in order to learn good policies …