Python reinforcement-learning openai-gym rl Tensorflow
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Description

Implementation of selected reinforcement learning algorithms in Tensorflow. A3C, DDPG, REINFORCE, DQN, etc.

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Implementations of Reinforcement Learning Algorithms in Python

Implementations of selected reinforcement learning algorithms with tensorflow. <!-- | Implemented Algorthms | Working Examples |-----------------|:--------------| | Policy Gradient Methods | | | REINFORCE with policy function approximation |

policy_gradient/cartpole_policy_gradient.py
| | REINFORCE with baseline |
policy_gradient/cartpole_reinforce_baseline.py
| | TD Learning | | | Standard epsilon greedy Q-learning |
TD/cartpole_qlearning.py
| | Deep Q-learning |
DQN/cartpole_dqn.py
| | Monte Carlo Methods | | | Monte Carlo (MC) estimation of action values |
monte_carlo/test_monte_carlo.py
| | Dynamic Programming MDP Solver | | | Value iteration |
DP/test_value_iteration.py
| | Policy iteration - policy evaluation & policy improvement |
DP/test_value_iteration.py
| -->

Implemented Algorithms

(Click into the links for more details)

Advanced
Policy Gradient Methods
Temporal Difference Learning
Monte Carlo Methods
Dynamic Programming MDP Solver

Environments

  • envs/gridworld.py
    : minimium gridworld implementation for testings

Dependencies

  • Python 2.7
  • Numpy
  • Tensorflow 0.12.1
  • OpenAI Gym (with Atari) 0.8.0
  • matplotlib (optional)

Tests

  • Files:
    test_*.py
  • Run unit test for [class]:

python test_[class].py
<!-- - Test coverage (requires
coverage
and
nose
):

nosetests --with-coverage --cover-package=.
-->

MIT License

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