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reinforcement_learning

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

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## Implementation of Reinforcement Learning Algorithms in Python

Implementation of selected reinforcement learning algorithms with tensorflow.

### Implemented Algorithms

(Click into the links for more details)

##### Advanced

- [Asynchronized Advantage Actor-Critic (A3C)](A3C/)
- [Deep Deterministic Policy Gradient (DDPG)](ddpg/)

##### Policy Gradient Methods

- [REINFORCE with policy function approximation](policy_gradient/)
- [REINFORCE with baseline](policy_gradient/reinforce_w_baseline.py)

##### Temporal Difference Learning

- [Standard epsilon greedy Q-learning](TD/qlearning.py)
- [Deep Q-learning](DQN/)

##### Monte Carlo Methods

- [Monte Carlo (MC) estimation of action values](monte_carlo/monte_carlo.py)

##### Dynamic Programming MDP Solver

- [Value iteration](DP/value_iteration.py)
- [Policy iteration - policy evaluation & policy improvement](DP/policy_iteration.py)

### 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

reinforcement_learning

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