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If you're not sure which to choose, learn more about installing packages. Uploaded Dec 9, source. Uploaded Dec 9, py3. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Dec 9, Navigation Project description Release history Download files. Project links Homepage github zenodo.

Meta License: Mozilla Public License 2. Maintainers ThomasCassimon. It is an array of x, y , treasure tuples. Each level should be a positive integer number. Upon creation, the numbers with inverted sign will be used for accelerating left and up, while the numbers themselves signify acceleration either right x or down y. The submarine finds a treasure 2. The maximum number of steps is reached, this option sets the maximum allowed number of steps. Action Space A tuple of 2, 1-hot encoded actions. Tuple [ Discrete , Discrete ] The first action is the acceleration in the X-dimensions, the second action is the acceleration in the Y-dimensions. Reward Space Box The reward for this environment is a 2-element vector.

The no-op action is always assumed to consume no fuel. Reward Space Box The reward for this environment is a 3-element vector. This allows the submarine to sit still and do nothing. Action Space The action space for the Vamplew wrapper consists of a single 1-hot encoded action. There are 4 or 5 possible actions to take, depending on how the wrapper was configured: Up Right Down Left Idle Actions are specified in this order. Observation Space Box The observation in this environment is a 2-element vector, containing the submarine's current row and column.

Project details Project links Homepage github zenodo. Download files Download the file for your platform. Source Distribution. This option serves two purposes: It shapes the seabed, and determines the value of each treasure. This option provides a list of discrete acceleration levels. When this option is enabled, the reward function will be reduced to below its minimum when the submarine causes a collision. ABOUT file for this package. Login to resync this project. Toggle navigation. SCA tools are cool, but are they enough? Join us Sept. Watch now!

Release 2. Tuple [ Discrete , Discrete ]. Releases 2. This option serves two purposes: It shapes the seabed, and determines the value of each treasure. It is an array of x, y , treasure tuples. This option provides a list of discrete acceleration levels. Each level should be a positive integer number. Upon creation, the numbers with inverted sign will be used for accelerating left and up, while the numbers themselves signify acceleration either right x or down y. When this option is enabled, the reward function will be reduced to below its minimum when the submarine causes a collision. The environment has 2 ending conditions: 1. The submarine finds a treasure 2.

The maximum number of steps is reached, this option sets the maximum allowed number of steps. The environment limits the absolute value of the submarine's maximum velocity to this number, to prevent overflow-related physics shenanigans from occurring. If this option is enabled, a grid will be shown when rendering the environment, showing the discrete spaces where the submarine can reside. If this option is enabled, the value of each treasure will be rendered on top of the treasure, making the rendering slightly clearer.

We allow the customization of the rendering through the use of the Theme class, in conjunction with the DeepSeaTreasureV0Renderer , this option allows the user to specify the Theme to use when rendering, the default Theme is returned by Theme. The no-op action is always assumed to consume no fuel. When true, this option enables a 5th action in this environment, idle. Learn more.

Please sign in to use Codespaces. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. There was a problem preparing your codespace, please try again. We introduce a new algorithm for multi-objective reinforcement learning MORL with linear preferences , with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple competing objectives whose relative importance preferences is unknown to the agent. While this alleviates dependence on scalar reward design, the expected return of a policy can change significantly with varying preferences, making it challenging to learn a single model to produce optimal policies under different preference conditions. We propose a generalized version of the Bellman equation to learn a single parametric representation for optimal policies over the space of all possible preferences.

After this initial learning phase, our agent can execute the optimal policy under any given preference, or automatically infer an underlying preference with very few samples. Experiments across four different domains demonstrate the effectiveness of our approach. PyTorch version for the code in synthetic was torch 0. The environment is modified from PyDial. PyTorch version for the code in pydial was torch 0. The multi-objective version SuperMario Game.

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