
  # **Q-Learning** Agent playing **FrozenLake-v1**
  This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
  

n_training_episodes = 200000  # Total training episodes <br>
learning_rate = 0.8          # Learning rate <br>

# Evaluation parameters
n_eval_episodes = 100        # Total number of test episodes <br>

# Environment parameters <br>
env_id = "FrozenLake-v1"     # Name of the environment <br>
max_steps = 100               # Max steps per episode <br>
gamma = 0.99                 # Discounting rate <br>
eval_seed = []               # The evaluation seed of the environment <br>

# Exploration parameters <br>
epsilon = 1.0                 # Exploration rate <br>
max_epsilon = 1.0             # Exploration probability at start <br>
min_epsilon = 0.05            # Minimum exploration probability <br>
decay_rate = 0.00005            # Exponential decay rate for exploration prob <br>
  
  ```
  
{"tags": ["FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-8x8-non_slippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-8x8-no_slippery", "type": "FrozenLake-v1-8x8-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward"}]}]}]}
FrozenLake-v1-8x8-no_slippery q-learning reinforcement-learning custom-implementation model-index region:us
TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us 


  # Q-Learning Agent playing FrozenLake-v1
  This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
  

n_training_episodes = 200000  # Total training episodes <br>
learning_rate = 0.8          # Learning rate <br>

# Evaluation parameters
n_eval_episodes = 100        # Total number of test episodes <br>

# Environment parameters <br>
env_id = "FrozenLake-v1"     # Name of the environment <br>
max_steps = 100               # Max steps per episode <br>
gamma = 0.99                 # Discounting rate <br>
eval_seed = []               # The evaluation seed of the environment <br>

# Exploration parameters <br>
epsilon = 1.0                 # Exploration rate <br>
max_epsilon = 1.0             # Exploration probability at start <br>
min_epsilon = 0.05            # Minimum exploration probability <br>
decay_rate = 0.00005            # Exponential decay rate for exploration prob <br>
  
  '''
  
# Q-Learning Agent playing FrozenLake-v1
  This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
  

n_training_episodes = 200000  # Total training episodes <br>
learning_rate = 0.8          # Learning rate <br> # Evaluation parameters
n_eval_episodes = 100        # Total number of test episodes <br> # Environment parameters <br>
env_id = "FrozenLake-v1"     # Name of the environment <br>
max_steps = 100               # Max steps per episode <br>
gamma = 0.99                 # Discounting rate <br>
eval_seed = []               # The evaluation seed of the environment <br> # Exploration parameters <br>
epsilon = 1.0                 # Exploration rate <br>
max_epsilon = 1.0             # Exploration probability at start <br>
min_epsilon = 0.05            # Minimum exploration probability <br>
decay_rate = 0.00005            # Exponential decay rate for exploration prob <br>
  
  '''
TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us 
 # Q-Learning Agent playing FrozenLake-v1
  This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
  

n_training_episodes = 200000  # Total training episodes <br>
learning_rate = 0.8          # Learning rate <br> # Evaluation parameters
n_eval_episodes = 100        # Total number of test episodes <br> # Environment parameters <br>
env_id = "FrozenLake-v1"     # Name of the environment <br>
max_steps = 100               # Max steps per episode <br>
gamma = 0.99                 # Discounting rate <br>
eval_seed = []               # The evaluation seed of the environment <br> # Exploration parameters <br>
epsilon = 1.0                 # Exploration rate <br>
max_epsilon = 1.0             # Exploration probability at start <br>
min_epsilon = 0.05            # Minimum exploration probability <br>
decay_rate = 0.00005            # Exponential decay rate for exploration prob <br>
  
  '''
passage: TAGS
#FrozenLake-v1-8x8-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us 
# Q-Learning Agent playing FrozenLake-v1
  This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
  

n_training_episodes = 200000  # Total training episodes <br>
learning_rate = 0.8          # Learning rate <br># Evaluation parameters
n_eval_episodes = 100        # Total number of test episodes <br># Environment parameters <br>
env_id = "FrozenLake-v1"     # Name of the environment <br>
max_steps = 100               # Max steps per episode <br>
gamma = 0.99                 # Discounting rate <br>
eval_seed = []               # The evaluation seed of the environment <br># Exploration parameters <br>
epsilon = 1.0                 # Exploration rate <br>
max_epsilon = 1.0             # Exploration probability at start <br>
min_epsilon = 0.05            # Minimum exploration probability <br>
decay_rate = 0.00005            # Exponential decay rate for exploration prob <br>
  
  '''