Updated versions of ML Agent notebooks , good place for starters. A Rainbow DQN implemented along side with n-step bootstrapping for best performance.
Features
Attributes:
max_priority (float): max priority
tree_ptr (int): next index of tree
alpha (float): alpha parameter for prioritized replay buffer
sum_tree (SumSegmentTree): sum tree for prior
min_tree (MinSegmentTree): min tree for min prior to get max weight
Attributes:
in_features (int): input size of linear module
out_features (int): output size of linear module
std_init (float): initial std value
weight_mu (nn.Parameter): mean value weight parameter
weight_sigma (nn.Parameter): std value weight parameter
bias_mu (nn.Parameter): mean value bias parameter
bias_sigma (nn.Parameter): std value bias parameter
Notice : Train is just 10 000 samples for test purpose. It can reach 0.9 benchmark value.
Set-up: A linear movement task where the agent must move left or right to rewarding states.
Goal: Move to the most reward state.
Agents: The environment contains one agent.
Agent Reward Function:
-0.01 at each step
+0.1 for arriving at suboptimal state.
+1.0 for arriving at optimal state.
Behavior Parameters:
Vector Observation space: One variable corresponding to current state.
Vector Action space: (Discrete) Two possible actions (Move left, move right).
Visual Observations: None
Float Properties: None
Benchmark Mean Reward: 0.93
In order to use it on local PC , you have to change strings.
Both builds are updated version of SDK , can swap easily.
