422 research outputs found
Deep Policies for Width-Based Planning in Pixel Domains
Width-based planning has demonstrated great success in recent years due to
its ability to scale independently of the size of the state space. For example,
Bandres et al. (2018) introduced a rollout version of the Iterated Width
algorithm whose performance compares well with humans and learning methods in
the pixel setting of the Atari games suite. In this setting, planning is done
on-line using the "screen" states and selecting actions by looking ahead into
the future. However, this algorithm is purely exploratory and does not leverage
past reward information. Furthermore, it requires the state to be factored into
features that need to be pre-defined for the particular task, e.g., the B-PROST
pixel features. In this work, we extend width-based planning by incorporating
an explicit policy in the action selection mechanism. Our method, called
-IW, interleaves width-based planning and policy learning using the
state-actions visited by the planner. The policy estimate takes the form of a
neural network and is in turn used to guide the planning step, thus reinforcing
promising paths. Surprisingly, we observe that the representation learned by
the neural network can be used as a feature space for the width-based planner
without degrading its performance, thus removing the requirement of pre-defined
features for the planner. We compare -IW with previous width-based methods
and with AlphaZero, a method that also interleaves planning and learning, in
simple environments, and show that -IW has superior performance. We also
show that -IW algorithm outperforms previous width-based methods in the
pixel setting of Atari games suite.Comment: In Proceedings of the 29th International Conference on Automated
Planning and Scheduling (ICAPS 2019). arXiv admin note: text overlap with
arXiv:1806.0589
Hierarchical Width-Based Planning and Learning
Width-based search methods have demonstrated state-of-the-art performance in
a wide range of testbeds, from classical planning problems to image-based
simulators such as Atari games. These methods scale independently of the size
of the state-space, but exponentially in the problem width. In practice,
running the algorithm with a width larger than 1 is computationally
intractable, prohibiting IW from solving higher width problems. In this paper,
we present a hierarchical algorithm that plans at two levels of abstraction. A
high-level planner uses abstract features that are incrementally discovered
from low-level pruning decisions. We illustrate this algorithm in classical
planning PDDL domains as well as in pixel-based simulator domains. In classical
planning, we show how IW(1) at two levels of abstraction can solve problems of
width 2. For pixel-based domains, we show how in combination with a learned
policy and a learned value function, the proposed hierarchical IW can
outperform current flat IW-based planners in Atari games with sparse rewards
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
Novelty and MCTS
Novelty search has become a popular technique in different fields such as evolutionary computing, classical AI planning, and deep reinforcement learning. Searching for novelty instead of, or in addition to, directly maximizing the search objective, aims at avoiding dead ends and local minima, and overall improving exploration. We propose and test the integration of novelty into Monte Carlo Tree Search (MCTS), a state-of-the-art framework for online RL planning, by linearly combining value estim
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