messed things up, repushing

.gitignore

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+*.pyc
+dist/
+gym_pacman.egg-info

README.md

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+# OpenAI Gym Berkeley-Pacman 
+
+Port of Berkeley AI Pacman to an OpenAI Gym environment.
+
+The Berkeley Pacman AI engine is provided at http://ai.berkeley.edu/project_overview.html
+
+## Info
+This repository provides two environments:
+* **BerkeleyPacman-v0**: which provides the Berkeley Pacman AI interface as is, with a randomly chosen layout. Layouts are in layouts/ folder
+* **BerkeleyPacmanPO-v0**: which provides the Berkeley Pacman AI interface with only a partially observable portion of the map (immediate nearby locations in a 3x3 grid)
+
+## Usage
+To use these environments, do:
+~~~~
+import gym
+import gym_pacman
+env = gym.make('BerkeleyPacman-v0')
+~~~~
+
+Note: This was created for the 10-703 Project at Carnegie Mellon University. This is still under active development

gym_pacman/VERSION

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+v1.001

gym_pacman/__init__.py

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+from gym.envs.registration import register
+
+register(
+    id='BerkeleyPacman-v0',
+    entry_point='gym_pacman.envs:PacmanEnv',
+)
+
+register(
+    id='BerkeleyPacmanPO-v0',
+    entry_point='gym_pacman.envs:PartiallyObservablePacmanEnv',
+)

gym_pacman/envs/__init__.py

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+from gym_pacman.envs.pacman_env import PacmanEnv, PartiallyObservablePacmanEnv

gym_pacman/envs/analysis.py

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+# analysis.py
+# -----------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# ([email protected]) and Dan Klein ([email protected]).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel ([email protected]).
+
+
+######################
+# ANALYSIS QUESTIONS #
+######################
+
+# Set the given parameters to obtain the specified policies through
+# value iteration.
+
+def question2():
+    answerDiscount = 0.9
+    answerNoise = 0.2
+    return answerDiscount, answerNoise
+
+def question3a():
+    answerDiscount = None
+    answerNoise = None
+    answerLivingReward = None
+    return answerDiscount, answerNoise, answerLivingReward
+    # If not possible, return 'NOT POSSIBLE'
+
+def question3b():
+    answerDiscount = None
+    answerNoise = None
+    answerLivingReward = None
+    return answerDiscount, answerNoise, answerLivingReward
+    # If not possible, return 'NOT POSSIBLE'
+
+def question3c():
+    answerDiscount = None
+    answerNoise = None
+    answerLivingReward = None
+    return answerDiscount, answerNoise, answerLivingReward
+    # If not possible, return 'NOT POSSIBLE'
+
+def question3d():
+    answerDiscount = None
+    answerNoise = None
+    answerLivingReward = None
+    return answerDiscount, answerNoise, answerLivingReward
+    # If not possible, return 'NOT POSSIBLE'
+
+def question3e():
+    answerDiscount = None
+    answerNoise = None
+    answerLivingReward = None
+    return answerDiscount, answerNoise, answerLivingReward
+    # If not possible, return 'NOT POSSIBLE'
+
+def question6():
+    answerEpsilon = None
+    answerLearningRate = None
+    return answerEpsilon, answerLearningRate
+    # If not possible, return 'NOT POSSIBLE'
+
+if __name__ == '__main__':
+    print 'Answers to analysis questions:'
+    import analysis
+    for q in [q for q in dir(analysis) if q.startswith('question')]:
+        response = getattr(analysis, q)()
+        print '  Question %s:\t%s' % (q, str(response))