12,812 research outputs found
Prioritizing starting states for reinforcement learning
Online, off-policy reinforcement learning algorithms are able to use an experience memory to remember and replay past experiences. In prior work, this approach was used to stabilize training by breaking the temporal correlations of the updates and avoiding the rapid forgetting of possibly rare experiences. In this work, we propose a conceptually simple framework that uses an experience memory to help exploration by prioritizing the starting states from which the agent starts acting in the environment, importantly, in a fashion that is also compatible with on-policy algorithms. Given the capacity to restart the agent in states corresponding to its past observations, we achieve this objective by (i) enabling the agent to restart in states belonging to significant past experiences (e.g., nearby goals), and (ii) promoting faster coverage of the state space through starting from a more diverse set of states. While, using a good priority measure to identify significant past transitions, we expect case (i) to more considerably help exploration in certain domains (e.g., sparse reward tasks), we hypothesize that case (ii) will generally be beneficial, even without any prioritization. We show empirically that our approach improves learning performance for both off-policy and on-policy deep reinforcement learning methods, with most notable gains in highly sparse reward tasks
Exploring Restart Distributions
We consider the generic approach of using an experience memory to help
exploration by adapting a restart distribution. That is, given the capacity to
reset the state with those corresponding to the agent's past observations, we
help exploration by promoting faster state-space coverage via restarting the
agent from a more diverse set of initial states, as well as allowing it to
restart in states associated with significant past experiences. This approach
is compatible with both on-policy and off-policy methods. However, a caveat is
that altering the distribution of initial states could change the optimal
policies when searching within a restricted class of policies. To reduce this
unsought learning bias, we evaluate our approach in deep reinforcement learning
which benefits from the high representational capacity of deep neural networks.
We instantiate three variants of our approach, each inspired by an idea in the
context of experience replay. Using these variants, we show that performance
gains can be achieved, especially in hard exploration problems.Comment: RLDM 201
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
With advances in reinforcement learning (RL), agents are now being developed
in high-stakes application domains such as healthcare and transportation.
Explaining the behavior of these agents is challenging, as the environments in
which they act have large state spaces, and their decision-making can be
affected by delayed rewards, making it difficult to analyze their behavior. To
address this problem, several approaches have been developed. Some approaches
attempt to convey the behavior of the agent, describing the
actions it takes in different states. Other approaches devised
explanations which provide information regarding the agent's decision-making in
a particular state. In this paper, we combine global and local explanation
methods, and evaluate their joint and separate contributions, providing (to the
best of our knowledge) the first user study of combined local and global
explanations for RL agents. Specifically, we augment strategy summaries that
extract important trajectories of states from simulations of the agent with
saliency maps which show what information the agent attends to. Our results
show that the choice of what states to include in the summary (global
information) strongly affects people's understanding of agents: participants
shown summaries that included important states significantly outperformed
participants who were presented with agent behavior in a randomly set of chosen
world-states. We find mixed results with respect to augmenting demonstrations
with saliency maps (local information), as the addition of saliency maps did
not significantly improve performance in most cases. However, we do find some
evidence that saliency maps can help users better understand what information
the agent relies on in its decision making, suggesting avenues for future work
that can further improve explanations of RL agents
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Testing in Continuous Integration (CI) involves test case prioritization,
selection, and execution at each cycle. Selecting the most promising test cases
to detect bugs is hard if there are uncertainties on the impact of committed
code changes or, if traceability links between code and tests are not
available. This paper introduces Retecs, a new method for automatically
learning test case selection and prioritization in CI with the goal to minimize
the round-trip time between code commits and developer feedback on failed test
cases. The Retecs method uses reinforcement learning to select and prioritize
test cases according to their duration, previous last execution and failure
history. In a constantly changing environment, where new test cases are created
and obsolete test cases are deleted, the Retecs method learns to prioritize
error-prone test cases higher under guidance of a reward function and by
observing previous CI cycles. By applying Retecs on data extracted from three
industrial case studies, we show for the first time that reinforcement learning
enables fruitful automatic adaptive test case selection and prioritization in
CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017).
Reinforcement Learning for Automatic Test Case Prioritization and Selection
in Continuous Integration. In Proceedings of 26th International Symposium on
Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
Shaping in humans and animals has been shown to be a powerful tool for
learning complex tasks as compared to learning in a randomized fashion. This
makes the problem less complex and enables one to solve the easier sub task at
hand first. Generating a curriculum for such guided learning involves
subjecting the agent to easier goals first, and then gradually increasing their
difficulty. This paper takes a similar direction and proposes a dual curriculum
scheme for solving robotic manipulation tasks with sparse rewards, called
MaMiC. It includes a macro curriculum scheme which divides the task into
multiple sub-tasks followed by a micro curriculum scheme which enables the
agent to learn between such discovered sub-tasks. We show how combining macro
and micro curriculum strategies help in overcoming major exploratory
constraints considered in robot manipulation tasks without having to engineer
any complex rewards. We also illustrate the meaning of the individual curricula
and how they can be used independently based on the task. The performance of
such a dual curriculum scheme is analyzed on the Fetch environments.Comment: To appear in the Proceedings of the 18th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2019). (Extended Abstract
Vision-based reinforcement learning using approximate policy iteration
A major issue for reinforcement learning (RL) applied to robotics is the time required to learn a new skill. While RL has been used to learn mobile robot control in many simulated domains, applications involving learning on real
robots are still relatively rare. In this paper, the Least-Squares Policy Iteration (LSPI) reinforcement learning algorithm and a new model-based algorithm Least-Squares Policy Iteration with Prioritized Sweeping (LSPI+), are implemented on a mobile robot to acquire new skills quickly and efficiently. LSPI+ combines the benefits of LSPI and prioritized sweeping, which uses all previous experience to focus the computational effort on the most āinterestingā or dynamic parts of the state space.
The proposed algorithms are tested on a household vacuum
cleaner robot for learning a docking task using vision as the only sensor modality. In experiments these algorithms are compared to other model-based and model-free RL algorithms. The results show that the number of trials required to learn the docking task is significantly reduced using LSPI compared to the other RL algorithms investigated, and that LSPI+ further improves on the performance of LSPI
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