In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies. We propose an unsupervised learning scheme, based on asymmetric self-play from (2018), that automatically learns a good representation of sub-goals in the environment and a low-level policy that can execute them. A high-level policy can then direct the lower one by generating a sequence of continuous sub-goal vectors. We evaluate our model using Mazebase and Mujoco environments, including the challenging AntGather task. Visualizations of the sub-goal embeddings reveal a logical decomposition of tasks within the environment. Quantitatively, our approach obtains compelling performance gains over non-hierarchical approaches.
Many recent works have considered options discovery via parameterized modules operating at different timescales, where an actor'' operates at a finer timescale than a manager'', which outputs a goal or target for the actor. For example, Vezhnevets al @cite_0 trains the actor and manager together end-to-end via reward from the environment.
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Temporally extended actions (e.g., macro actions) have proven very useful for speeding up learning, ensuring robustness and building prior knowledge into AI systems. The options framework (Precup, 2000; Sutton, Precup & Singh, 1999) provides a natural way of incorporating such actions into reinforcement learning systems, but leaves open the issue of howgood options might be identified. In this paper, we empirically explore a simple approach to creating options. The underlying assumption is that the agent will be asked to perform different goalachievement tasks in an environment that is otherwise the same over time. Our approach is based on the intuition that states that are frequently visited on system trajectories, could prove to be useful subgoals (e.g., McGovern & Barto, 2001; Iba, 1989).We propose a greedy algorithm for identifying subgoals based on state visitation counts. We present empirical studies of this approach in two gridworld navigation tasks. One of the environments we explored contains bottleneck states, and the algorithm indeed finds these states, as expected. The second environment is an empty gridworld with no obstacles. Although the environment does not contain any obvious subgoals, our approach still finds useful options, which essentially allow the agent to explore the environment more quickly. Abstract With regard to future service robots, unsafe exceptional circumstances can occur in complex systems that are hardly to foresee. In this paper, the assumption of having no knowledge about the environment is investigated using reinforcement learning as an option for learning behavior by trial-and-error. In such a scenario, action-selection decisions are made based on future reward predictions for minimizing costs in reaching a goal. It is shown that the selection of safety-critical actions leading to highly negative costs from the environment is directly related to the exploration exploitation dilemma in temporal-difference learning. For this, several exploration policies are investigated with regard to worst- and best-case performance in a dynamic environment. Our results show that in contrast to established exploration policies like e-Greedy and Softmax, the recently proposed VDBE-Softmax policy seems to be more appropriate for such applications due to its robustness of the exploration parameter for unexpected situations. When describing robot motion with dynamic movement primitives (DMPs), goal (trajectory endpoint), shape and temporal scaling parameters are used. In reinforcement learning with DMPs, usually goals and temporal scaling parameters are predefined and only the weights for shaping a DMP are learned. Many tasks, however, exist where the best goal position is not a priori known, requiring to learn it. Thus, here we specifically address the question of how to simultaneously combine goal and shape parameter learning. This is a difficult problem because learning of both parameters could easily interfere in a destructive way. We apply value function approximation techniques for goal learning and direct policy search methods for shape learning. Specifically, we use ''policy improvement with path integrals'' and ''natural actor critic'' for the policy search. We solve a learning-to-pour-liquid task in simulations as well as using a Pa10 robot arm. Results for learning from scratch, learning initialized by human demonstration, as well as for modifying the tool for the learned DMPs are presented. We observe that the combination of goal and shape learning is stable and robust within large parameter regimes. Learning converges quickly even in the presence of disturbances, which makes this combined method suitable for robotic applications. This paper presents a novel algorithm for learning parameters in statistical dialogue systems which are modelled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy which selects the system’s responses based on the inferred state; and a reward function which specifies the desired behaviour of the system. Ideally both the model parameters and the policy would be designed to maximise the reward function. However, whilst there are many techniques available for learning the optimal policy, there are no good ways of learning the optimal model parameters that scale to real-world dialogue systems. The Natural Belief-Critic (NBC) algorithm presented in this paper is a policy gradient method which offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected reward. The resulting gradient is then used to adapt the prior distribution of the dialogue model parameters. The algorithm is evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximise the reward function result in significantly improved performance compared to the baseline handcrafted parameters.