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  • Infinite Mario RL

    Infinite Mario RL

    This environment is derived from the Infinite Mario domain from RLCompetition2009 (based on RL-Glue) and uses SML to connect to Soar.

    Agents play a variant of Super Mario, a complete side-scrolling video game with destructible blocks, enemies, fireballs, coins, chasms, platforms, etc. The state space is complicated, but factored in an object-oriented way, which captures many aspects of the real world. Challenges include:
    • Path planning: How can Mario navigate around simple obstacles,
    ...
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  • Water Jug (Reinforcement Learning)

    Water Jug (Reinforcement Learning)

    This agent demonstrates how to solve the water jug problem using reinforcement learning. It is a modification of the Simple Water Jug Agent. Two main changes are made: templates are used to generate the full space of possible moves that the agent can perform and rewards are set based on the problem solution.

    Soar capabilities
    • Reinforcement learning
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated Publications
    • The
    ...
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  • Taxi (Hierarchical Reinforcement Learning)

    Taxi (Hierarchical Reinforcement Learning)

    This agent simulates an omniscient taxi driver that uses reinforcement learning and hierarchical task decomposition to improve its performance over runs.

    Soar capabilities
    • Hierarchical task decomposition
    • Reinforcement learning
    Download Links
    • The agent is included in the Taxi download.
    External EnvironmentDefault Rules
    • None.
    Associated Publications...
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  • Taxi (Reinforcement Learning)

    Taxi (Reinforcement Learning)

    This agent simulates an omniscient taxi driver that uses reinforcement learning to improve its performance over runs.

    Soar capabilities
    • Reinforcement Learning
    Download Links
    • The agent is included in the Taxi download.
    External EnvironmentDefault Rules
    • None.
    Associated PublicationsDevel...
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  • Blocks-World (Reinforcement Learning)

    Blocks-World (Reinforcement Learning)

    This agent contains a version of blocks-world that uses reinforcement learning for move-block, which moves a block to a destination.

    Soar capabilities
    • Reinforcement Learning
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated PublicationsDeveloper
    • John Laird
    Soar Versions
    • Soar 8,9
    Project Type
    • VisualSoar
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  • Blocks-World (Subgoaling with RL)

    Blocks-World (Subgoaling with RL)

    agent incorporates both operator subgoaling/means ends analysis with reinforcement learning. All search control knowledge (operator evaluation rules) are removed from blocks-world-operator-subgoaling and instead there are RL rules supplemented with rules to compute reward, both in the top state and the substate. Implemented for four blocks.

    Soar capabilities
    • Subgoaling with means-ends analysis
    • Reinforcement learning
    Download LinksExternal En...
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  • Blocks-World (Look-Ahead with RL)

    Blocks-World (Look-Ahead with RL)

    This agent contains a version of blocks-world that involves one level of problem spaces and look-ahead but with two important extensions. It demonstrates how RL-rules can be learned by chunking and then updated in the future. The advantage over simple lookahead is that it doesn't lock on to the one path found during look-ahead after chunking. It will still do some exploration.

    Soar capabilities
    • Reinforcement Learning
    • Chunking
    • Look-ahead Subgoaling
    Download Links...
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  • Graph Search

    Graph Search

    An agent that performs graph search using the selection-astar default rules. This approach can be modified to use any of the different selection approaches.

    The basic idea behind this agent is that there is a mission to go from place to place (in the mission structure) using go-to-location for each go-to-location use an iterative form of A* search to find the minimal path between each place iteratively select go-to-waypoint to move through the graph
    Key data structures (initialized...
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  • Infinite Mario RL

    Infinite Mario RL

    Various agents for learning and playing Infinite Mario domain from RLCompetition 2009. They encode different state representations and learning strategies.

    Soar capabilities
    • Reinforcement learning
    Downloads
    • This agent is packaged with the Infinite Mario environment download.
    External EnvironmentDefault Rules
    • None.
    Documentation
    • None
    Associated Publications
    • Mohan, S. and Laird, J. E. (2011). An Object-Oriented Approach to Reinforcement Learning in an Action Game.
    ...
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  • Left-Right Reinforcement Learning Agent

    Left-Right Reinforcement Learning Agent

    A very simple agent that demonstrates reinforcement learning. Agent chooses between left and right and must learn that left is preferred.

    Soar capabilities
    • Reinforcement learning
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated Publications
    • Soar Reinforcement Learning Tutorial
    Developer
    • John Laird
    Soar Versions
    • Soar 9
    Project Type
    • VisualSoar
    See more | Go to post

  • Reinforcement Learning Unit Test

    Reinforcement Learning Unit Test

    An agent that tests four rl-rule sequence cases to behaviorally test the Soar reinforcement learning update mechanism. Learning/discount-rate are set to known values and other parameters can be tweaked. Some of the expected behavior is documented in the README file included.

    Soar capabilities
    • Reinforcement learning
    Download LinksExternal Environment
    • None.
    Default Rules
    • None.
    Associated Publications
    • None.
    Developer
    • John Laird
    Soar Ve...
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