Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are independent of the agents' intentions. Moreover, by capturing the diffusion process of a random walk, different options act at different time scales, making them helpful for exploration strategies. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.
Our algorithm can be seen as a approach, in which options are constructed before the agent observes any informative reward. These options are composed to generate the desired policy. Options discovered this way tend to be independent of an agent's intention, and are potentially useful in many different tasks @cite_12 . Such options can also be seen as being useful for exploration by allowing agents to commit to a behavior for an extended period of time @cite_0 . Among the approaches to discover options without using extrinsic rewards are the use of global or local graph centrality measures @cite_10 @cite_21 @cite_4 and clustering of states @cite_17 @cite_13 @cite_25 . Interestingly, and also use the graph Laplacian in their algorithm, but to identify bottleneck states.
@cite_13 @cite_4 @cite_21 @cite_0 @cite_10 @cite_25 @cite_12 @cite_17
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In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termination states. To this end, we instantiate two policy gradient based algorithms, one that creates an explicit embedding space of options and one that represents options implicitly. The algorithms also provide an explicit measure of empowerment in a given state that can be used by an empowerment maximizing agent. The algorithm scales well with function approximation and we demonstrate the applicability of the algorithm on a range of tasks. 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. We consider a graph theoretic approach for automatic construction of options in a dynamic environment. A map of the environment is generated on-line by the learning agent, representing the topological structure of the state transitions. A clustering algorithm is then used to partition the state space to different regions. Policies for reaching the different parts of the space are separately learned and added to the model in a form of options (macro-actions). The options are used for accelerating the Q-Learning algorithm. We extend the basic algorithm and consider building a map that includes preliminary indication of the location of "interesting" regions of the state space, where the value gradient is significant and additional exploration might be beneficial. Experiments indicate significant speedups, especially in the initial learning phase. Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to alleviate these concerns. Instead of learning a single monolithic policy, the agent learns a set of simpler sub-policies as well as the initiation and termination probabilities for each of those sub-policies. While existing option learning algorithms frequently require manual specification of components such as the sub-policies, we present an algorithm which infers all relevant components of the option framework from data. Furthermore, the proposed approach is based on parametric option representations and works well in combination with current policy search methods, which are particularly well suited for continuous real-world tasks. We present results on SMDPs with discrete as well as continuous state-action spaces. The results show that the presented algorithm can combine simple sub-policies to solve complex tasks and can improve learning performance on simpler tasks.